ALBERT

Description: Presents corrections to the paper, "Estimation of forest biomass from two-level model inversion of single-pass InSAR data" (Soja, M.J., et al.,Trans. Geosci. Remote Sens., vol. 53, no. 9, pp. 5083???5099, Sep. 2015).

Description: The temporal variations (diurnal and annual) in arboreal $(varepsilon_mathrm{Tree})$ and bare soil $(varepsilon_mathrm{Soil})$ dielectric constants and their correlation with precipitation were examined for several trees in Japan. A significant (1 $sigma$ (standard deviation) and 2 $sigma$ ) $varepsilon_mathrm{Tree}$ increase is observed after rainfall at 89.8% and 90.5% probability. However, rainfall does not always induce significant $varepsilon_mathrm{Tree}$ increases. Rainfall of more than 5 mm/day can induce 1 $sigma$ $varepsilon_mathrm{Tree}$ Tree increase at a 59.6% probability. In order to examine whether the increase in $varepsilon_mathrm{Tree}$ affects the L-band $sigma^{0}$ variation in a forest, the four-year temporal variation of the L-band backscattering coefficient $(sigma^{0})$ was estimated from observations by the Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar. Observed maximum absolute deviations from the mean over the forest area were 1.0 and 1.2 dB for $sigma_{mathrm{HH}}^{0}$ and $sigma_mathrm{HV}^{0}$ , respectively, and 4.0 and 3.0 dB over open land. $sigma^{0}$ and rainfall correlations show that $varepsilon_mathrm{Tree}$ and $sigma_mathrm{Forest}^{0}$ are proportional to precipitation integrated over seven or eight days; $varepsilon_mathrm{Soil}$ and $sigma_mathrm{Open land}^{0}$ are proportional to precipitation integrated over three days. This finding indicates that $varepsilon_mathrm{Tree}$ variations influence $sigma_{mathrm{Forest areas}}^{0}$ . A stronger correlation between $sigma_mathrm{HV}^{0}$ and precipitation is observed in several sites with low $sigma_mathrm{HV}^{0}$ , where less biomass is expected, and several sites with high $sigma_mathrm{HV}^{0}$ , where more biomass is expected. A weaker correlation between $sigma_mathrm{HV}^{0}$ and precipitation is observed for several sites with high $sigma_mathrm{HV}^{0}$ . These differences may be explained by the different contributions of double bounce scattering and potential transpiration, which is a measure of the ability of the atmosphere to remove water from the surface th

Description: The spectral and radiometric quality of airborne imaging spectrometer data is affected by the anisotropic reflectance behavior of the imaged surface. Illumination and observation angle-dependent patterns of surface reflected radiation propagate into products, hinder quantitative assessment of biophysical/biochemical parameters, and decrease the comparability of data from multiple flight lines. The Ross–Li model, originally developed for multiangular observations, can be inverted to estimate and correct for surface anisotropy effects. This requires land cover be stratified into distinct types of scattering behavior. When the observations subsumed in these classes cover a range of view angles, a pseudo multiangular view on the surface can be employed to invert the Ross–Li model. A discrete land cover classification, however, bears the risk of inappropriate scattering correction resulting in spatial artifacts in the corrected data, predominantly in transition regions of two land cover types (e.g., soil and sparse vegetation with varying fractions). We invert the Ross–Li model on continuous land cover fraction layers. We decompose land cover in dominating structural types using linear spectral unmixing. Ross–Li kernel weights and formulations are estimated for each type independently; the correction is then applied pixel-wise according to the fractional distribution. The corrected Airborne Prism EXperiment imaging spectrometer data show significant reduction of anisotropic reflectance effects of up to 90% (average 60% to 75%, $p=0.05$ ), measured in the overlapping regions of adjacent flight lines. No spatial artifacts or spectral irregularities are observed after correction.

Description: Most spaceborne sensors have a tradeoff between high spatial and high temporal resolutions. This tradeoff limits the use of remote sensing data in various applications that require images in both the high spatial and high temporal resolutions. In this paper, we propose a novel technique to create a fine spatial and high temporal resolution images at a ground-based data processing system. Resourcesat-2 is one of the Indian Space Research Organization missions, and it carries the Linear Imaging Self-Scanning sensors (LISS III and LISS IV) and an Advanced Wide-Field Sensor (AWiFS) onboard. The spatial resolution of LISS III is 23.5 m, and that of AWiFS is 56 m. The temporal resolution of LISS III is 24 days, and that of AWiFS is five days. The proposed method creates a synthetic LISS-III image at 23.5-m spatial and five-day temporal resolutions. It is based on the subpixel relationship between a single AWiFS–LISS-III image pair, which is acquired before or after the prediction date. In temporal data composition, spurious spatial discontinuities are inevitable for land-cover type changes. These discontinuities were identified with temporal edge primitives and were smoothed with a spatial-profile-averaging method. A synthetic LISS-III image for time $t_{k}$ is predicted from an AWiFS image at time $t_{k}$ and a single AWiFS–LISS-III image pair at time $t_{0}$ , where $t_{0}ne t_{k}$ . Experimental results demonstrated that the proposed method is superior in terms of the computational efficiency and prediction accuracy with the other existing methods.

Description: Glacier avalanches are natural hazards that could damage infrastructures and threaten lives in high-altitude mountainous terrains. On April 7, 2012, a massive ice avalanche struck a Pakistani base at Gayari sector, Saltoro Valley, and buried/killed 148 soldiers and civilians. Keeping in view the catastrophe, a study was designed with the objectives to: 1) model and simulate the Gayari sector glacier avalanche incident in terms of height, extent, velocity, pressure, and momentum; 2) generate hazard risk assessment of possible other glacier avalanches in the Saltoro Valley through modeling and simulation; and 3) suitability analysis of current camp sites and recommendation of new safe camps sites locations in the Saltoro valley. To simulate the Gayari sector glacier event and other Glacier possible avalanches, a physical process-based rapid mass movements (RAMMS) was used. The RAMMS has two main components, i.e., Voellmy–Salm (VS) model and random kinetic energy, which deals with variables such as avalanche height and the mean avalanche velocity during the course of simulation. The suitability analysis of current camp sites were achieved using weighted overlay analysis with different constraints in ArcGIS Spatial Analyst. The RAMMS model simulation of the Gayari avalanche event predicted a maximum velocity of 74 ms -1 , generating a pressure of 5074 kPa and attaining a height of 45 m, whereas the predicted debris volume on the ground was 3.8145 million m 3 . A good agreement was found between actual debris height and extent, as compared with the RAMMS model output. The RAMMS model simulated all the potential tributary glacier avalanches of Saltoro valley very well. The weighted overlay analyses in ArcGIS Spatial Analyst revealed that the existing camp sites are safe and were not threatened by the glacier avalanche hazard. However, it was recommended that the Gayari camp should not be constructed at the same location and should be relocated- to the proposed safe camp sites identified in this research study. The proposed methodology developed in the current study could be applied in the Siachen conflict zone for avalanche hazard/risk analysis of all the camp sites located in the valley.

Description: Object-oriented remote sensing image classification is becoming more and more popular because it can integrate spatial information from neighboring regions of different shapes and sizes into the classification procedure to improve the mapping accuracy. However, object identification itself is difficult and challenging. Superpixels, which are groups of spatially connected similar pixels, have the scale between the pixel level and the object level and can be generated from oversegmentation. In this paper, we establish a new classification framework using a superpixel-based graphical model. Superpixels instead of pixels are applied as the basic unit to the graphical model to capture the contextual information and the spatial dependence between the superpixels. The advantage of this treatment is that it makes the classification less sensitive to noise and segmentation scale. The contribution of this paper is the application of a graphical model to remote sensing image semantic segmentation. It is threefold. 1) Gradient fusion is applied to multispectral images before the watershed segmentation algorithm is used for superpixel generation. 2) A probabilistic fusion method is designed to derive node potential in the superpixel-based graphical model to address the problem of insufficient training samples at the superpixel level. 3) A boundary penalty between the superpixels is introduced in the edge potential evaluation. Experiments on three real data sets were conducted. The results show that the proposed method performs better than the related state-of-the-art methods tested.

Description: We introduce a practical and accurate model, referred to as “GO4,” to describe near-nadir microwave scattering from the sea surface, and at the same time, we address the issue of the filtered mean square slope (mss) conventionally used in the geometrical optics model. GO4 is a simple correction of this last model, taking into account the diffraction correction induced by the rough surface through what we call an effective mean square curvature (msc). We evaluate the effective msc as a function of the surface wavenumber spectrum and the radar frequency and show that GO4 reaches the same accuracy as the physical optics model in a wide range of incidence and frequency bands with the sole knowledge of the mss and msc parameters. The key point is that the mss entering in GO4 is not the filtered but the total slope. We provide estimation of the effective msc on the basis of classical sea spectrum models. We also evaluate the effective msc from near-nadir satellite data in various bands and show that it is consistent with model predictions. Non-Gaussian effects are discussed and shown to be incorporated in the effective msc. We give some applications of the method, namely, the estimation of the total sea surface mss and the recalibration of relative radar cross sections.

Description: This paper evaluates the radiometric accuracy of observations from the Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar-orbiting Partnership and Sondeur Atmospherique du Profil d' Humidité Intropicale par Radiométrie (SAPHIR) onboard Megha-Tropiques through intercalibration and validation versus in situ radiosonde and Global Positioning System Radio Occultation (GPS-RO) observations. SAPHIR and ATMS water vapor channels operate at slightly different frequencies. We calculated the bias due to radiometric errors as the difference between the observed and simulated differences between the two instruments. This difference, which is often referred to as double difference, ranges between 0.3 and 0.7 K, which shows good consistency between the instruments. We used a radiative transfer model to simulate the satellite brightness temperatures (Tbs) using radiosonde and GPS-RO profiles and then compared simulated and observed Tbs. The difference between radiosonde and ATMS Tbs for the middle and upper tropospheric temperature sounding channels was less than 0.5 K at most stations, but the difference between radiosonde and ATMS/SAPHIR Tbs for water vapor channels was between 0.5 and 2.0 K. The larger bias for the water vapor channels is mainly due to several errors in radiosonde humidity observations. The mean differences between the ATMS observations and the Tbs simulated using GPS-RO profiles were 0.2, 0.3, 0.4, 0.2, and −0.2 K for channels 10–14, respectively; and the uncertainty increases from 0.02 K for channel 10 to 0.07 K for channel 14.

Description: This paper presents a completely automatic processing chain for orthorectification of optical pushbroom sensors. The procedure is robust and works without manual intervention from raw satellite image to orthoimage. It is modularly divided in four main steps: metadata extraction, automatic ground control point (GCP) extraction, geometric modeling, and orthorectification. The GCP extraction step uses georeferenced vector roads as a reference and produces a file with a list of points and their accuracy estimation. The physical geometric model is based on collinearity equations and works with sensor-corrected (level 1) optical satellite images. It models the sensor position and attitude with second-order piecewise polynomials depending on the acquisition time. The exterior orientation parameters are estimated in a least squares adjustment, employing random sample consensus and robust estimation algorithms for the removal of erroneous points and fine-tuning of the results. The images are finally orthorectified using a digital elevation model and positioned in a national coordinate system. The usability of the method is presented by testing three RapidEye images of regions with different terrain configurations. Several tests were carried out to verify the efficiency of the procedure and to make it more robust. Using the geometric model, subpixel accuracy on independent check points was achieved, and positional accuracy of orthoimages was around one pixel. The proposed procedure is general and can be easily adapted to various sensors.

Description: This paper presents a new ground moving target imaging (GMTIm) algorithm for airborne synthetic aperture radar (SAR) based on a novel time-frequency representation (TFR), Lv's distribution (LVD). We first analyze generic moving target signatures for a multichannel SAR and then derive the analytical spectrum of a point target moving at a constant velocity by a polar format algorithm for SAR image formation. SAR motion deviation from a predetermined flight track is considered to facilitate airborne SAR applications. LVD, as a recently developed TFR for the analysis of multicomponent linear-frequency-modulated signal, is adopted to represent the target kinematic spectrum in the Doppler centroid frequency and chirp rate domain. As a result, the proposed SAR-GMTIm algorithm is capable of imaging multiple moving targets even when they are located at the same range resolution cell. Some practical issues such as imaging maneuvering targets and small/weak targets are discussed to enhance the applicability of the proposed algorithm. Simulation results with isotropic point moving targets are presented to validate the effectiveness and superiority of the proposed algorithm. Raw data collected by an airborne multichannel SAR are also used to verify the performance improvement made by the proposed algorithm.

Description: Satellite soil moisture estimates have received increasing attention over the past decade. This paper examines the applicability of estimating soil moisture states and soil hydraulic parameters through two particle filter (PF) methods: The PF with commonly used sampling importance resampling (PF-SIR) and the PF with recently developed Markov chain Monte Carlo sampling (PF-MCMC) methods. In a synthetic experiment, the potential of assimilating remotely sensed near-surface soil moisture measurements into a 1-D mechanistic soil water model (HYDRUS-1D) using both the PF-SIR and PF-MCMC algorithms is analyzed. The effects of satellite temporal resolution and accuracy, soil type, and ensemble size on the assimilation of soil moisture are analyzed. In a real data experiment, we first validate the Advanced Microwave Scanning Radiometer--Earth Observing System (AMSR-E) soil moisture products in the Oklahoma Little Washita Watershed. Aside from rescaling the remotely sensed soil moisture, a bias correction algorithm is implemented to correct the deep soil moisture estimate. Both the ascending and descending AMSR-E soil moisture data are assimilated into the HYDRUS-1D model. The synthetic assimilation results indicated that, whereas both updating schemes showed the ability to correct the soil moisture state and estimate hydraulic parameters, the PF-MCMC scheme is consistently more accurate than PR-SIR. For real data case, the quality of remotely sensed soil moisture impacts the benefits of their assimilation into the model. The PF-MCMC scheme brought marginal gains than the open-loop simulation in RMSE at both surface and root-zone soil layer, whereas the PF-SIR scheme degraded the open-loop simulation.

Description: We propose a new deterministic approach for remote sensing retrieval, called modified total least squares (MTLS), built upon the total least squares (TLS) technique. MTLS implicitly determines the optimal regularization strength to be applied to the normal equation first-order Newtonian retrieval using all of the noise terms embedded in the residual vector. The TLS technique does not include any constraint to prevent noise enhancement in the state space parameters from the existing noise in measurement space for an inversion with an ill-conditioned Jacobian. To stabilize the noise propagation into parameter space, we introduce an additional empirically derived regularization proportional to the logarithm of the condition number of the Jacobian and inversely proportional to the L2-norm of the residual vector. The derivation, operational advantages and use of the MTLS method are demonstrated by retrieving sea surface temperature from GOES-13 satellite measurements. An analytic equation is derived for the total retrieval error, and is shown to agree well with the observed error. This can also serve as a quality indicator for pixel-level retrievals. We also introduce additional tests from the MTLS solutions to identify contaminated pixels due to residual clouds, error in the water vapor profile and aerosols. Comparison of the performances of our new and other methods, namely, optimal estimation and regression-based retrieval, is performed to understand the relative prospects and problems associated with these methods. This was done using operational match-ups for 42 months of data, and demonstrates a relatively superior temporally consistent performance of the MTLS technique.

Description: Radar-based remote sensing for measurement of ocean surface waves presents advantages over conventional point sensors such as wave buoys. As its use becomes more widespread, it is important to understand the sensitivity of the extracted wave parameters to the characteristics of the radar and the scatterers. To examine such issues, experiments were performed offshore of the Scripps Institution of Oceanography pier in July 2010. Radar measurements in low wind speeds were performed with a dual-polarized high-resolution X-band pulse-Doppler radar at low grazing angles along with two independent measurements of the surface waves using conventional sensors, a GPS-based buoy, and an ultrasonic array. Comparison between radar cross section (RCS) and Doppler modulations shows peak values occurring nearly in-phase, in contrast with tilt modulation theory. Spectral comparisons between Doppler-based and RCS-based spectra show that Doppler-based spectra demonstrate greater sensitivity to swell-induced modulations, whereas RCS-based spectra show greater sensitivity to small-scale modulations (or generally have more noise at high frequency), and they equally capture energy at the wind wave peak. Doppler estimates of peak period were consistent with the conventional sensors, whereas the RCS differed in assignment of peak period to wind seas rather than swell in a couple of cases. Higher order period statistics of both RCS and Doppler were consistent with the conventional sensors. Radar-based significant wave heights are lower than buoy-based values and contain nontrivial variability of ∼33%. Comparisons between HH and VV polarization data show that VV data more accurately represent the wave field, particularly as the wind speeds decrease.

Description: Polarimetric synthetic aperture radar satellite and ground-based Ku- and X-band scatterometer measurements are used to explore the scattering mechanism for ice in shallow Arctic lakes, wherein strong radiometric responses are seen for floating ice, and low returns are evident where the ice has grounded. Scatterometer measurements confirm that high backscatter is from the ice/water interface, whereas polarimetric decomposition suggests that the dominant scattering mechanism from that interface is single bounce. Using Fresnel equations, a simple model for surface bounce from the ice/water interface is proposed, and its predictions are supported by experimental parameters such as co-pol phase difference, co-pol ratio, and the results of rigorous numerical modeling. Despite early research suggesting double-bounce scattering from columnar air bubbles and the ice/water interface as the dominant scattering mechanism in shallow lakes, this paper strongly supports a single-bounce model.

Description: The on-orbit radiometric calibration of the reflective solar bands (RSBs) of the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership satellite is carried out primarily through observations of a fully illuminated solar diffuser (SD) panel. Accurate knowledge of the solar spectral radiance scattered from the SD is available. The sensor aperture spectral radiance is assumed to be a quadratic polynomial function of a VIIRS detector's background-subtracted response in digital number. The coefficients of the polynomial were initially determined prelaunch. Once on orbit, we assume that these coefficients change uniformly by a common calibration factor, which is referred to as the $F$ -factor. The known solar spectral radiance scattered from a fully illuminated SD allows for the determination of these $F$ -factors. We describe the methodology and the associated algorithms used in the calculation of the RSB $F$ -factors. Our results show that the $F$ -factors change over time, with the largest change occurring at a wavelength of 862 nm (with a value of about 1.55 on day 950 after the satellite launch, relative to its value at the beginning of the launch) . In addition, we estimate the relative error standard deviations of the computed top-of-the-atmosphere reflectance at the detector pixel level. On day 950 of the mission, the relative error standard deviations are all less or equal to 0.016, except for the M11 band (band central wavelength of 2257 nm) , which has a relative error standard deviation of about 0.049 due to a very low signal-to-noise ratio.

Description: We present simplified expressions for the cross-polarized backscatter of a randomly rough surface predicted by the second-order small-slope approximation (SSA2). The simplification is based on appropriate polynomial approximations of the SSA2 kernel function. We obtain numerically efficient expressions for the cross-polarized backscattering amplitude of a deterministic surface in the form of a single space integral involving only the surface elevation and the second (mixed) derivative of the surface elevation. The ensemble average normalized radar cross section is then derived under a Gaussian random process assumption for the surface. The resulting expression has the form of a Kirchhoff integral involving the roughness correlation function and its second- and fourth-order cross-derivatives. Further simplification is achieved for off-nadir observations using a high-frequency approximation; the result is an analytical formula involving only the resonant curvature and the radar-filtered mean square slope in the out-of-plane direction. A numerical validation of the simplified expressions is provided by comparison with exact SSA2 predictions in representative test cases. The dependence of cross-polarized backscattering on the incidence angle as well as wind speed and direction is then investigated for the case of a directional sea surface model. At near nadir incidence, a clear maximum in azimuth of the cross-polarized backscatter is observed for radar look directions 45 $^{circ}$ from the wind direction.

Description: The synoptic determination of ocean circulation using the data acquired from space, with a coherent depiction of its turbulent characteristics, remains a fundamental challenge in oceanography. This determination has the potential of revealing all aspects of the ocean dynamic variability on a wide range of spatiotemporal scales and will enhance our understanding of ocean–atmosphere exchanges at superresolution, as required in the present context of climate change. Here, we show a four-year time series of spatial superresolution (4 km) turbulent ocean dynamics generated from satellite data using emerging ideas in signal processing coming from nonlinear physics, low-resolution dynamics, and superresolution oceanic sea surface temperature data acquired from optical sensors. The method at its core consists in propagating across the scales the low-resolution dynamics in a multiresolution analysis computed on adimensional critical transition information.

Description: This paper presents the theory, algorithm, and results of a new bistatic interferometry synthetic aperture radar (InSAR) method. It employs the data acquired in an innovative bistatic configuration, which uses the orbital sensors as transmitters of opportunity and the stationary receivers on the ground, to generate a digital elevation model (DEM). In the bistatic spaceborne/stationary InSAR configuration, the interferometric phase only depends on the target-receiver range, which could not be obtained directly from the measured bistatic range. Therefore, the conventional transforming relationship between the interferometric phase and the topographic height is no longer practical. In order to solve the problem, we introduce a new conversion relationship between the interferometric phase and the topographic height, which is derived by the model of the ellipsoidal projection in the bistatic configuration. Meanwhile, the error analysis of the new conversion is carried out through a simulation. Both the simulated and measured data are used to test and verify the feasibility of the new bistatic InSAR method. In the spaceborne/stationary InSAR experiment, YaoGan-3 (an L-band spaceborne SAR system launched by China) was selected as the transmitter and two stationary receivers were mounted on the top of a tall building. The generated DEM of high quality shows that the presented method performs very well in the bistatic InSAR data process.

Description: We present a nonparametric Bayesian hierarchical model (HDP_IBPs) to classify very high resolution panchromatic satellite images in an unsupervised way, in which the hierarchical Dirichlet process (HDP) and Indian buffet process (IBP) are combined on multiple scenes. The main contribution of this paper is a novel application framework to solve the problems of traditional probabilistic topic models and achieve the effective unsupervised classification of very high resolution (VHR) panchromatic satellite images. In this framework, a VHR satellite image is first oversegmented into basic processing units and divided into a set of subimages. We use the Chinese restaurant franchise process as a construct method of the HDP to capture the latent semantic structures (i.e., classes) and the class proportion (i.e., co-occurrence of topics) for each subimage. Meanwhile, the subimages are grouped into different scenes based on the class proportion. Finally, the IBP is employed to select the most appropriate classes for each subimage from all of the classes based on different scenes in turn. The hierarchical structure of our model transmits the spatial information from the original image to the scene layer implicitly and provides useful cues of classification. The experimental results show that HDP_IBPs outperforms state-of-the-art models in terms of both qualitative and quantitative evaluations.

Description: This paper describes a linear-image-transform-based algorithm for reducing stripe noise, track line artifacts, and motion-induced errors in remote sensing data. Developed for multibeam bathymetry (MB), the method has also been used for removing scalloping in synthetic aperture radar images. The proposed image transform is the composition of an invertible edge detection operator and a fast discrete Radon transform (DRT) due to Götz, Druckmüller, and Brady. The inverse DRT is computed by using an iterative method and exploiting an approximate inverse algorithm due to Press. The edge operator is implemented by circular convolution with a Laplacian point spread function modified to render the operator invertible. In the transformed image, linear discontinuities appear as high-intensity spots, which may be reset to zero. In MB data, a second noise signature is linked to motion-induced errors. A Chebyshev approximation of the original image is subtracted before applying the transform, and added back to the denoised image; this is necessary to avoid boundary effects. It is possible to process data faster and suppress motion-induced noise further by filtering images in nonoverlapping blocks using a matrix representation for the inverse DRT. Processed test images from several MB data sets had less noise and distortion compared with those obtained with standard low-pass filters. Denoising also improved the accuracy in statistical classification of geomorphological type by 10–28% for two sets of invariant terrain features.

Description: Sparse graph embedding (SGE) is a promising technique useful for the nonlinear feature extraction (FE) of hyperspectral images (HSIs). However, such images exhibit spatial variability and spectral multimodality, presenting challenges to existing FE methods, including SGE. To address this issue, this paper presents two novel SGE methods for HSI classification. One method, which is termed simultaneous SGE (SSGE), is designed to consider the spatial variability of spectral signatures by using a simultaneous sparse representation (SSR) model integrated with a shape-adaptive neighborhood building approach. In addition, a sparse graph is constructed via matrix computation based on sparse codes. Then, low-dimensional features are produced by employing linear graph embedding (LGE) based on the constructed sparse graph. The other method, which is termed simultaneous sparse multimanifold learning (SSMML), is proposed to handle the multimodality of an HSI. In SSMML, multiple views are generated to represent different modalities. Then, multiview-oriented submanifolds are produced by adopting SSGE, and they are further integrated via coregularization. SSGE is capable of modeling both local and global data structures. Furthermore, SSMML serves as a prototype that can model multimodal data structures. The proposed methods are evaluated by using sparse multinomial logistic regression for HSI classification. Experimental results with two popular hyperspectral data sets validate the good performance of the two methods in producing more representative low-dimensional features and yielding superior classification results compared with other related approaches.

Description: We present a new algorithm that simultaneously retrieves aerosol properties and land surface bidirectional reflectance distribution function (BRDF) over Australian from Advanced Along-Track Scanning Radiometer images. Three key components are addressed: 1) an analytical radiative transfer formulation, based on Green's function, linking top-of-atmosphere (TOA) reflectance to the surface BRDF; 2) a novel approach to modeling BRDF using an extended compositing period; and 3) a set of representative aerosol models based on a published typology of Australian aerosols. Due to the generally low aerosol loadings and widespread bright surfaces over Australia, BRDF modeling is crucial. By using a 9-month compositing period, 90% of the Australian continent can be modeled with an error in the forward-to-nadir reflectance ratio of 2.5% or less. Comparison with suitably processed Moderate Resolution Imaging Spectroradiometer BRDF/albedo products demonstrates satisfactory agreement. For the studying period from 2002 to 2008, validation of aerosol optical depth (AOD) against eight sun photometers across Australia encompassing widely different atmospheric and surface regimes shows high accuracy, with a mean absolute error in AOD at 550 nm of 0.03 and a bias of 0.007. About 60% of the matchup points are within an absolute error of 0.03, 80% are within 0.05, and 96% are within 0.1. The algorithm selects for each cell an optimal aerosol type from a set of four predefined candidate models. Continental aerosol maps derived from the new method indicate broadscale agreement with known seasonal aerosol sources, while providing new insights into the spatial and temporal distributions of aerosol over the Australian continent.

Description: This investigation examines the phenomenology effects of the squint angle on the morphology of moving target smears in spotlight synthetic aperture radar (SAR). This analysis includes both the smears resulting from standard image formation applied to simulated radar measurements as well as the theoretical predictions for the central contours of the signatures. In particular, this paper generates the down-range and cross-range components of the predicted central 2-D contours of mover signatures, including the locations of the cross-range offsets. The analytics for squinted geometry include additional contributions in the signature contour equations that do not arise for the case of broadside imaging. These terms can affect the overall contour morphology, particularly in terms of shape and extent. Numerous examples are presented to demonstrate that the signature prediction equations yield excellent agreement with standard image formation with simulated radar data. Therefore, this analysis can provide an effective tool in predicting the shape, extent, and location of smears due to arbitrarily moving surface targets for squinted spotlight SAR.

Description: In this paper, we consider signal subspace estimation based on low-rank representation for hyperspectral imagery. It is often assumed that major signal sources occupy a low-rank subspace. Due to the mixed nature of hyperspectral remote sensing data, the underlying data structure may include multiple subspaces instead of a single subspace. Therefore, in this paper, we propose the use of low-rank subspace representation to estimate the number of subspaces in hyperspectral imagery. In particular, we develop simple estimation approaches without user-defined parameters because these parameters can be fixed as constants. Both real data experiments and computer simulations demonstrate excellent performance of the proposed approaches over those currently in the literature.

Description: The conventional spaced-receiver approach uses amplitude scintillations to estimate equatorial ionospheric irregularity drift velocities. This approach is less applicable at high latitudes where there is a lack of substantial amplitude scintillations. This paper presents a method to estimate ionosphere irregularity horizontal drift velocities based on GPS signal carrier phase measurements. Joint time–frequency analysis of the carrier phase measurements using an adaptive periodogram technique generates time-varying spectrograms of ionospheric irregularity-induced phase fluctuations. Cross correlation of the spectrograms between antenna pairs provides time lag information on propagating radio signals through the same ionospheric structure. The time lag information is combined with known positions of the receiver array, satellite orbits, and assumed irregularity altitude to infer ionospheric irregularity horizontal drift velocity. This paper presents the methodology and demonstrates its feasibility using data collected by a GPS receiver array at Gakona, Alaska. The potential error sources of this method are also analyzed.

Description: Fractional vegetation cover (FVC) plays an important role in earth surface process simulations, climate modeling, and global change studies. Several global FVC products have been generated using medium spatial resolution satellite data. However, the validation results indicate inconsistencies, as well as spatial and temporal discontinuities of the current FVC products. The objective of this paper is to develop a reliable estimation algorithm to operationally produce a high-quality global FVC product from the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance. The high-spatial-resolution FVC data were first generated using Landsat TM/ETM+ data at the global sampling locations, and then, the general regression neural networks (GRNNs) were trained using the high-spatial-resolution FVC data and the reprocessed MODIS surface reflectance data. The direct validation using ground reference data from validation of land European Remote Sensing instruments sites indicated that the performance of the proposed method ( $mathbf{R}^{2}=0.809$ , $mathbf{RMSE} =0.157$ ) was comparable with that of the GEOV1 FVC product ( $mathbf{R}^{2}=0.775$ , $mathbf{RMSE} =0.166$ ) , which is currently considered to be the best global FVC product from SPOT VEGETATION data. Further comparison indicated that the spatial and temporal continuity of the estimates from the proposed method was superior to that of the GEOV1 FVC product.

Description: This paper proposes a region kernel to measure the region-to-region distance similarity for hyperspectral image (HSI) classification. The region kernel is designed to be a linear combination of multiscale box kernels, which can handle the HSI regions with arbitrary shape and size. Integrating labeled pixels and labeled regions, we further propose a region-kernel-based support vector machine (RKSVM) classification framework. In RKSVM, three different composite kernels are constructed to describe the joint spatial–spectral similarity. Particularly, we design a desirable stack composite kernel that consists of the point-based kernel, the region-based kernel, and the cross point-to-region kernel. The effectiveness of the proposed RKSVM is validated on three benchmark hyperspectral data sets. Experimental results show the superiority of our region kernel method over the classical point kernel methods.

Description: Atmospheric refraction in the troposphere causes the propagation speed of electromagnetic signals to be less than the light speed. This creates a difference between the actual propagation path delay and the distance of the geometrical straight-line path, i.e., a quantity known as the tropospheric delay. As classical imaging algorithms for spaceborne synthetic aperture radar (SAR) do not take the tropospheric delay into account, imaging filters are designed based on the assumption of rectilinear propagation with the light speed. Therefore, a residual phase exists in imaging results, which affects focusing quality under the condition of high resolution. In order to compensate for the impact of tropospheric delay on focusing performance, this paper modifies the spaceborne SAR echo model and then proposes an imaging compensation algorithm. The key to this algorithm is to fit a range delay coefficient based on the European Geostationary Navigation Overlay Service model of zenith delay and Niell mapping function, which projects the zenith delay onto the looking direction. After range compensation, classical imaging, and azimuth compensation, which compose the proposed algorithm, the processed results are well focused.

Description: Accurate correction of atmospheric effects on data captured by an infrared (IR) camera is crucial for several applications such as vegetation monitoring, temperature monitoring, satellite images, hyperspectral imaging, numerical model simulations, surface properties characterization, and IR measurement interpretation. Atmospheric effects depend on the temporal changes, i.e., year, season, day, hour, etc., and on the geometry between the camera and the measured scene, i.e., line of sight. The orientation and the optical depth of the camera significantly affect the variation of the geometry across the pixels. In this paper, we propose a method to estimate the range and zenith angle of each pixel using only the Global Positioning System (GPS) coordinates of the camera and a point of interest in the scene. The estimated geometry and measured meteorological data are used to obtain the spectral atmospheric transmittance and path radiance. Furthermore, we propose an atmospheric effects removal, i.e., atmospheric correction, method that considers the spectral characteristics of the detector, lens, and filter. The proposed atmospheric correction process is analyzed in detail with the simultaneous measurements of two IR cameras. In this process, an enhanced temperature calibration method is developed and it is shown that the temperature accuracy for the dynamic range of the IR camera is very close to the noise equivalent temperature difference (NETD) value of the camera.

Description: Multiple scattering within a mixed pixel results in a nonlinear effect on the measured spectra in remotely sensed imagery. This study provides a quantitative assessment of multiple scattering in the reflectance of semiarid shrublands and explores its relationship to the characteristics of shrubs (density and height) and imaging parameters (wavelength and viewing angles). Field measurements were conducted at the southern fringe of the Otindag Sandy Land in China. A Monte Carlo ray tracing model, the Forest LIGHT interaction model (FLIGHT), was applied to simulate the multiple scattering results. FLIGHT simulation results were first evaluated against field measurements and then compared with a Landsat-8 OLI image. Results show that: 1) the contribution of multiple scattering to the spectra of a scene increases linearly with the fractional cover of vegetation and crown height; 2) in general, multiple scattering has a stronger effect on the near-infrared (NIR) domain than on the visible bands; 3) shadows significantly strengthen the multiple scattering effect, specifically within the visible bands; and 4) 80 to 100% of the total multiple scattering is caused by the second-order scattering within the visible bands and 60% to 90% within the NIR band. This study helps to improve our understanding of the multiple scattering effect and to select between linear and nonlinear spectral unmixing models to solve the abundances of shrubs and soil in mixed pixels.

Description: The main objective of the Chinese Chang'E-3 (CE-3) lunar satellite is to achieve soft-landing and roving exploration on the lunar surface. A multibeam radar in the lunar lander is implemented to measure the echoes from the lunar rough surface during its descending and to derive the speed of the lander. In this paper, numerical simulation of multiangular radar echoes and speed inversions from Doppler frequency are presented. An area of the Lunar Sinus Iridum bay, as landing site, is specifically selected. The rough surface described with the real DTM data is first divided into triangular patches for numerical Kirchhoff approximation calculation. The radar echoes of multiangular radar beams of CE-3 during the landing are numerically simulated. The echo phase and the Doppler frequency are then derived to obtain the vertical speed.

Description: Light scattering models of snow are very important for the remote sensing of snow. Many previous models have used unrealistic assumptions about the snow particle shape and microstructure. In this paper, a new model is proposed, wherein a bicontinuous medium is used to simulate the snow microstructure, and geometric optics theory is used in combination with the Monte Carlo method to simulate the scattering properties of snow. Then, using the radiative transfer equation, the snow reflectance, including the polarized reflectance, can be simulated. Unlike other models that use Monte Carlo ray tracing, the new model is computationally efficient and can be used for massive simulations and practical applications. The simulation results of the new model are compared with the ground measurements and simulation results of a traditional model based on the Mie theory. Through validations and comparisons, the new model is shown to demonstrate a significantly improved capability in simulating the bidirectional reflectance of snow. The importance of the grain shape and microstructure modeling in the light scattering models of snow is confirmed by the comparison of the simulation results.

Description: In microwave radiometric remote sensing, undetected radio frequency interference (RFI) can adversely affect the accuracy of the science products. A method is presented to adaptively tune the parameters of an RFI detection algorithm which controls the equivalent brightness temperature of undetected RFI. The method is adaptive in the sense that it adjusts to variations in the RFI environment, e.g., from high RFI conditions near some population centers to low RFI conditions in the tropical Pacific Ocean. The RFI environment is characterized by inferring the distribution of low-level undetected RFI from that of high-level detected RFI using appropriate scaling arguments. The resulting tuned algorithm adjusts its detection threshold to equalize the brightness temperature calibration bias due to RFI at the expense of the now variable measurement precision (noise equivalent delta temperature). This tradeoff between calibration bias and measurement precision can be represented as a modified version of the classic receiver-operating-characteristic curve. The radiometer on the Aquarius/SAC-D mission is used as an example.

Description: Polarimetric technology has been one of the most important advances in microwave remote sensing during recent decades. H-alpha decomposition, which is a type of polarimetric analysis technique, has been common for terrain and land-use classification in polarimetric synthetic aperture radar. However, the technique has been less common in the ground penetrating radar (GPR) community. In this paper, we apply the H-alpha decomposition to analyze the surface GPR data to obtain polarimetric attributes for subsurface target classification. Also, by combining H-alpha decomposition and migration, we can obtain a subsurface H-alpha color-coded reconstructed target image, from which we can use both the polarimetric attributes and the geometrical features of the subsurface targets to enhance the ability of subsurface target classification of surface GPR. A 3-D full polarimetric GPR data set was acquired in a laboratory experiment, in which four targets, a scatterer with many branches, a ball, a plate, and a dihedral scatter, were buried in dry sand under flat ground surface, and used to test these techniques. As results, we obtained the subsurface H-alpha distribution and classified the subsurface targets. Also, we derived a subsurface H-alpha color-coded reconstructed target image and identified all four targets in the laboratory experiment.

Description: Snow microwave radiance assimilation (RA) or brightness temperature data assimilation (DA) has shown promise for improving snow water equivalent (SWE) estimation. A successful RA study requires, however, an analysis of the error characteristics of coupled land surface-radiative transfer models (LSM/RTMs). This paper focuses on the Community Land Model version 4 (CLM4) as the land-surface model and on the microwave emission model for layered snowpacks (MEMLS) and the dense media radiative transfer multilayer (DMRT-ML) model as RTMs. Using the National Aeronautics and Space Administration Cold Land Processes Field Experiment (CLPX) data sets and through synthetic experiments, the errors of the coupled CLM4/DMRT-ML and CLM4/MEMLS are characterized by: 1) evaluating the CLM4 snowpack state simulations; 2) assessing the performance of RTMs in simulating the brightness temperature $(T_{B} )$ ; and 3) analyzing the correlations between the SWE error $(varepsilon_text{SWE})$ and the $T_{B}$ error $(varepsilon_T_{B} ) $ from the RA perspective. The results using the CLPX data sets show that, given a large error of the snow grain radius $(varepsilon_r_{e} )$ under dry snowpack conditions (along with a small error of the snow temperature $(varepsilon_T_mathrm{snow})$ ), the correlations between $varepsilon_text{SWE}$ and $varepsilon_T_{B} $ are mainly determined by the relationship between $varepsilon_r_{e}$ and the snow depth error $(varepsilon_d_{mathrm{snow}})$ or the snow density error $(varepsilon_rho_{mathrm{snow}} ) $ . The synthetic experiments were carried out for the CLPX region (shallow snowpack conditions and the Rocky Mountains (deep snowpack conditions using the atmospheric ensemble reanalysis produced by the coupled DA Research Testbed/Community Atmospheric Model (CAM4. The synthetic experiments support the results from the CLPX data sets and show that the errors of soil (the water content and the temperature, snow wetness, and snow temperature mostly result in positive correlations between $varepsilon_text{SWE}$ and $varepsilon_T_{B} $ . CLM4/DMRT-ML and CLM4/MEMLS tend to produce varying RA performance, with more positive and negative correlations between $varepsilon_text{SWE}$ and $varepsilon_T_{B} $ , respectively. These results suggest the necessity of using multiple snowpack RTMs in RA to improve the SWE estimation at the continental scale. The results in this paper also show that the magnitude of $varepsilon_r_{e}$ and its relationship to $varepsilon_text{SWE} $ are important for the RA performance. Most of the SWE estimations in RA are improved when $varepsilon_text{SWE}$ and

Description: This paper addresses the important yet unresolved problem of estimating forest properties from polarimetric-interferometric radar images affected by temporal decorrelation. We approach the problem by formulating a physical model of the polarimetric-interferometric coherence that incorporates both volumetric and temporal decorrelation effects. The model is termed random-motion-over-ground (RMoG) model, as it combines the random-volume-over-ground (RVoG) model with a Gaussian-statistic motion model of the canopy elements. Key features of the RMoG model are: 1) temporal decorrelation depends on the vertical structure of forests; 2) volumetric and temporal coherences are not separable as simple multiplicative factors; and 3) temporal decorrelation is complex-valued and changes with wave polarization. This third feature is particularly important as it allows compensating for unknown levels of temporal decorrelation using multiple polarimetric channels. To estimate model parameters such as tree height and canopy motion, we propose an algorithm that minimizes the least square distance between model predictions and complex coherence observations. The algorithm was applied to L-band NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar data acquired over the Harvard Forest (Massachussetts, USA). We found that the RMS difference at stand level between estimated RMoG-model tree height and NASA's lidar Laser Vegetation and Ice Sensor tree height was within 12% of the lidar-derived height, which improved significantly the RMS difference of 37% obtained using the RVoG model and ignoring temporal decorrelation. This result contributes to our ability of estimating forest biomass using in-orbit and forthcoming polarimetric-interferometric radar missions.

Description: It is believed that it is essential to take the spatial adaptivity into the segmentation method for polarimetric synthetic aperture radar (PolSAR) images. The size and shape of each segment and the strength of the relationship of neighboring pixels need to depend on the local spatial complexity of the scene. The wedgelet framework provides a promising analysis tool for spatial information. The major advantage of the wedgelet analysis is that it captures the geometrical structure of images at multiple scales, with the local spatial complexity taken into consideration. Hence, in this paper, we propose a wedgelet approximation and analysis framework specially designed for PolSAR data. Based on this framework, a spatially adaptive representation and segmentation method is constructed and presented. It mainly consists of three parts: first, the multiscale wedgelet decomposition is applied to the PolSAR image, and the local geometrical information is captured in an optimal way; then, the image is segmented in a spatially adaptive manner by the multiscale wedgelet representation in the form of the regularized optimization, which keeps a balance between the approximation and parsimony of the representation; the final part is the spatial-complexity-adaptive segmentation refinement based on the Wishart Markov random field model. The performance of the proposed method is presented and analyzed on two experimental data sets, with visual presentation and numerical evaluation. It is also compared with an existing and theoretically well-founded segmentation method. The experiments and results demonstrate the availability and advantage of the proposed method.

Description: In inverse synthetic aperture radar (ISAR) imaging of nonuniformly rotating targets, such as highly maneuvering airplanes and ships fluctuating with oceanic waves, azimuth echoes have to be modeled as cubic phase signals (CPSs) after the range migration compensation and the translational-induced phase error correction. For the CPS model, the chirp rate and the quadratic chirp rate, which deteriorate the azimuth focusing quality due to the Doppler frequency shift, need to be estimated with a parameter estimation algorithm. In this paper, by employing the proposed generalized scaled Fourier transform (GSCFT) and the nonuniform fast Fourier transform (NUFFT), a fast parameter estimation algorithm is presented and utilized in the ISAR imaging of the nonuniformly rotating target. Compared to the scaled Fourier transform-based algorithm, advantages of the fast parameter estimation algorithm include the following: 1) the computational cost is lower due to the utilization of the NUFFT, and 2) the GSCFT has a wider applicability in ISAR imaging applications. The CPS model and the algorithm implementation are verified with the real radar data of a ship target. In addition, the cross-term, which plays an important role in correlation algorithms, is analyzed for the fast parameter estimation algorithm. Through simulations of the synthetic data and the real radar data, we verify the effectiveness of the fast parameter estimation algorithm and the corresponding ISAR imaging algorithm.

Description: Classification is one of the most important techniques to the analysis of hyperspectral remote sensing images. Nonetheless, there are many challenging problems arising in this task. Two common issues are the curse of dimensionality and the spatial information modeling. In this paper, we present a new general framework to train series of effective classifiers with spatial information for classifying hyperspectral data. The proposed framework is based on the two key observations: 1) the curse of dimensionality and the high feature-to-instance ratio can be alleviated by using random subspace (RS) ensembles; and 2) the spatial–contextual information is modeled by the extended multiattribute profiles (EMAPs). Two fast learning algorithms, i.e., decision tree (DT) and extreme learning machine (ELM), are selected as the base classifiers. Six RS ensemble methods, namely, RS with DT, random forest (RF), rotation forest, rotation RF (RoRF), RS with ELM (RSELM), and rotation subspace with ELM (RoELM), are constructed by the multiple base learners. Experimental results on both simulated and real hyperspectral data verify the effectiveness of the RS ensemble methods for the classification of both spectral and spatial information (EMAPs). On the University of Pavia Reflective Optics Spectrographic Imaging System image, our proposed approaches, i.e., both RSELM and RoELM with EMAPs, achieve the state-of-the-art performances, which demonstrates the advantage of the proposed methods. The key parameters in RS ensembles and the computational complexity are also investigated in this paper.

Description: The Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission was recommended by the National Research Council in 2007 to conduct highly accurate and International System of Unit-traceable decadal change observations and provide an on-orbit intercalibration standard with high accuracy for relevant Earth observing sensors. The goal of reference intercalibration is to enable rigorous observations of critical climate change variables, including reflected broadband radiation, cloud properties, and changes in surface albedo, including snow and ice albedo feedback, to be made consistently among different sensors. This requires the CLARREO Reflected Solar Spectrometer (RSS) to provide highly accurate spectral reflectance measurements to establish an on-orbit reference with a radiometric accuracy requirement better than 0.3% $(mathrm{k} =2) $ for existing sensors. In this paper, MODTRAN-simulated top-of-atmosphere spectral data and spectral measurements from the SCIAMACHY instrument on Envisat are used to determine sensitivity of intercalibration uncertainty on key design parameters of the CLARREO spectrometer: spectral range, sampling and resolution. Their impact on intercalibration uncertainty for MODIS and VIIRS imagers is estimated for various surface types (ocean, vegetation, desert, snow, deep convective clouds, clouds and all-sky) . Results indicate that for the visible to near-infrared spectral region (465–856 nm) , the RSS instrument under current design concept produces uncertainties of 0.16% for the spectral range and 0.3% for the sampling and resolution. However, for the water vapor absorption bands in the short wavelength infrared region (1242–1629 nm) , the same requirement is not met for sampling and resolution due to their high sensitivity to the influence of atmospheric water vapor.

Description: Generally, some object-based features are more relevant to a thematic class than other features. These strongly relevant features, termed as class-specific features, would significantly contribute to thematic information extraction for very high resolution (VHR) images. However, many existing feature selection methods have been designed to select a good feature subset for all classes, rather than an independent feature subset for the thematic class. The latter might better meet the requirement of thematic information extraction than the former. In addition, the lack of quantitative evaluation of the contribution of the selected features to thematic classes also weakens our understandability of these features. To address the problems, class-specific feature selection methods are developed to measure the effectiveness of features for extracting thematic information from VHR images. First, the one-versus-all scheme is combined with traditional feature selection methods, such as ReliefF and LeastC. Also, one-versus-one scheme is utilized for alleviating the negative impact of a class imbalance problem arising from the one-versus-all scheme. Then, the relative contributions of features to thematic classes are obtained by the class-specific feature selection methods to describe the effectiveness of features for thematic information extraction. Finally, the class-specific feature selection methods are compared with the original methods on three different VHR image data sets by the nearest neighbor and support vector machine. Experimental results show that the class-specific feature selection methods outperform the corresponding conventional methods, and the one-versus-one scheme surpasses one-versus-all scheme. Additionally, many features are evaluated by the class-specific feature selection methods, to provide end users advice on effectiveness of the features.

Description: The conditional random field (CRF) model is suitable for the image segmentation because this model relaxes the assumption of conditional independence of the observed data and models the data-dependent label interaction in the image modeling. However, this model has a limited ability to capture the global and local image information from the perspective of multiresolution analysis. Moreover, for synthetic aperture radar (SAR) image segmentation, SAR scattering statistics that are essential to SAR image processing are not considered in the CRF model. In this paper, we propose a hierarchical CRF (HIECRF) model for SAR image segmentation. The HIECRF model belongs to the discriminative models according to the semantic structure. While inheriting the advantages of the CRF model, the HIECRF model achieves the integration of the image features and SAR scattering statistics and captures the contextual structure information in the spatial and scale spaces. Moreover, we derive a hierarchical inference algorithm for the HIECRF model in virtue of the mean-field approximation (MFA) to provide the maximization of the posterior marginal (MPM) estimate of the HIECRF model. Then, by the bottom-up and the top-down recursions in the hierarchical inference procedure, the HIECRF model effectively exploits the global and local image information, including the contextual structures, the image features, and the scattering statistics, to achieve the MPM segmentation. The effectiveness of the HIECRF model is demonstrated by the application to the semisupervised segmentation of the simulated images and the real SAR images.

Description: Due to the limited spatial resolution of multispectral/hyperspectral data, mixed pixels widely exist and various spectral unmixing techniques have been developed for information extraction at the subpixel level in recent years. One of the challenging problems in spectral mixture analysis is how to model the data of a primary class. Given that the within-class spectral variability (WSV) is inevitable, it is more realistic to associate a group of representative spectra with a pure class. The unmixing method using the extended support vector machines (eSVMs) has handled this problem effectively. However, it has simplified WSV in the mixed cases. In this paper, a further development of eSVMs is presented to address two problems in multiple-endmember spectral mixture analysis: 1) one mixed pixel may be unmixed into different fractions (model overlap); and 2) one fraction may correspond to a group of mixed pixels (fraction overlap). Then, spectral unmixing resolution (SUR) is introduced to characterize how finely the mixture in a mixed pixel can be quantified. The quantitative relationship between SUR and WSV of endmembers is derived via a geometry analysis in support vector machine feature space. Thus, the possible SUR can be estimated when multiple endmembers for each class are given. Moreover, if the requirement of SUR is fixed, the acceptance level of WSV is then limited, which can be used as a guide to remove outliers and purify endmembers for each primary class. Experiments are presented to illustrate model and fraction overlap problems and the application of SUR in uncertainty analysis of spectral unmixing.

Description: We have studied thin ice detection using Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) and Special Sensor Microwave Imager/Sounder radiometer data acquired over the Barents and Kara Seas during three winters (November–April) in 2008–2011. Moderate Resolution Imaging Spectroradiometer-based ice thickness charts were used as reference data. Thin ice detection was studied using polarization and spectral gradient ratios (PR and GR) calculated from the 36/37 and 89/91 GHz radiometer data. Thresholds for thin ice detection and maximum thicknesses for the detected thin ice ( $hT$ ) were determined, as were error rates for misdetections. The results for different 1-D PR and GR parameters led to the conclusion that the AMSR-E PR36 and H-polarized GR8936 would be the best parameters for a 2-D classifier. We adopted the linear discrimination analysis (LDA) as a statistical tool. Thin ice areas with $hT$ of 30 cm could be separated from thicker ice fields with approximately 20% error level. In our large data set, the estimation of thin ice thickness was not possible with reasonable accuracy due to the large scatter between ice thickness and the PR and GR signatures. This is likely due to a large data set, besides thin ice in polynyas also thin ice in the marginal ice zone and thin ice from freeze-up period. The optimal LDA parameters in the classifier and $hT$ depended on the daily mean air temperature ( $(T_{am} )$ ). We could not yet parameterize the classifier optimally according to $(T_{am} ) $ , but the constructed classifier worked rather robustly as indicated by the relat- ve small error rate variation between the three analyzed winters.

Description: Hyperspectral images (HSIs) can be very noisy, and the amount of noise may differ from band to band. While some spectral bands may be dominated by low signal-independent noise levels, others have mixed noise levels, which may include high levels of Gaussian, Poisson, and Spike noises. When a denoising algorithm is globally applied to the whole data set, it usually affects the low-noise bands adversely. Therefore, it is better to use different criteria for denoising different bands. In this paper, we propose a new denoising strategy to do so. The method is based on a 2-D nonsubsampled shearlet transform, applied to each spectral band of the HSI. We propose an effective method to distinguish between bands with low levels of Gaussian noise (LGN bands) and bands with mixed noise (MN bands) based on spectral correlation. LGN bands are denoised using a thresholding technique on the shearlet coefficients. On the MN bands, a local noise reduction method is applied, in which the detail shearlet coefficients of adjacent LGN bands are employed. This targeted approach is prone to reduce spectral distortions during denoising compared with global denoising methods. This advantage is shown in experiments where the proposed method is compared with state-of-the-art denoising methods on synthetic and real hyperspectral data sets. To assess the effect of denoising, classification and spectral unmixing tasks are applied to the denoised data. Obtained results show the superiority of the proposed approach.

Description: Linear spectral unmixing aims at estimating the number of pure spectral substances, also called endmembers , their spectral signatures, and their abundance fractions in remotely sensed hyperspectral images. This paper describes a method for unsupervised hyperspectral unmixing called minimum volume simplex analysis (MVSA) and introduces a new computationally efficient implementation. MVSA approaches hyperspectral unmixing by fitting a minimum volume simplex to the hyperspectral data, constraining the abundance fractions to belong to the probability simplex. The resulting optimization problem, which is computationally complex, is solved in this paper by implementing a sequence of quadratically constrained subproblems using the interior point method, which is particularly effective from the computational viewpoint. The proposed implementation (available online: www.lx.it.pt/%7ejun/DemoMVSA.zip) is shown to exhibit state-of-the-art performance not only in terms of unmixing accuracy, particularly in nonpure pixel scenarios, but also in terms of computational performance. Our experiments have been conducted using both synthetic and real data sets. An important assumption of MVSA is that pure pixels may not be present in the hyperspectral data, thus addressing a common situation in real scenarios which are often dominated by highly mixed pixels. In our experiments, we observe that MVSA yields competitive performance when compared with other available algorithms that work under the nonpure pixel regime. Our results also demonstrate that MVSA is well suited to problems involving a high number of endmembers (i.e., complex scenes) and also for problems involving a high number of pixels (i.e., large scenes).

Description: Stereo images have long been the main practical data source for the high-accuracy retrieval of 3-D information over large areas. However, stereoscopy has been surpassed by laser scanning (LS) techniques in recent years, particularly in forested areas, because the reflection of laser points from object surfaces directly provides 3-D geometric features and because the laser beam has good penetration capacity through forest canopies. In the last few years, image-based point clouds have become a more widely available data source because of advances in matching algorithms and computer hardware. This paper explores the possibility of using consumer cameras for forest field data collection and presents an application of terrestrial image-based point clouds derived from a handheld camera to forest plot inventories. In the experiment, the sample forest plot was photographed in a stop-and-go mode using different routes and camera settings. Five data sets were generated from photographs taken in the field, representing different photographic conditions. The stem detection accuracy ranged between 60% and 84%, and the root-mean-square errors of the estimated diameters at breast height were between 2.98 and 6.79 cm. The performance of image-based point clouds in forest data collection was compared with that of point clouds derived from two LS techniques, i.e., terrestrial LS (the professional level) and personal LS (an emerging technology). The study indicates that the construction of image-based point clouds of forest field data requires only low-cost, low-weight, and easy-to-use equipment and automated data processing. Photographic measurement is easy and relatively fast. The accuracy of tree attribute estimates is close to an acceptable level for forest field inventory but is lower than that achieved with the tested LS techniques.

Description: Ice sounding radars are able to measure ice sheets by profiling their glaciological features from the surface to the bedrock. The current airborne and, in particular, future space-based systems are suffering from off-nadir surface clutter, which can mask the depth signal of interest. The most recent surface clutter suppression techniques are based on multi-phase-center systems combined with sophisticated coherent postprocessing. The performance of the techniques can be improved by accurate direction-of-arrival (DOA) estimates of the surface clutter. This paper deals with data-driven DOA estimation for surface clutter signals, which includes a formulation of the mathematical foundation of spatial aliasing. DOA estimation is applied to data acquired with the P-band POLarimetric Airborne Radar Ice Sounder at the Jutulstraumen Glacier, Antarctica. The effects of spatial aliasing related to a large phase center spacing are analyzed, and an unwrapping procedure is presented and applied to the data. Finally, DOA estimation of full-scene data is analyzed and used to show an along-track and incidence (off-nadir) angle dependent variation of the effective scattering center of the surface return, which is caused by a varying penetration depth.

Description: A model for aboveground biomass estimation from single-pass interferometric synthetic aperture radar (InSAR) data is presented. Forest height and canopy density estimates $Delta h$ and $eta_0$ , respectively, obtained from two-level model (TLM) inversion, are used as biomass predictors. Eighteen bistatic VV-polarized TanDEM-X (TDM) acquisitions are used, made over two Swedish test sites in the summers of 2011, 2012, and 2013 (nominal incidence angle: 41 $^{circ} $ ; height-of-ambiguity: 32–63 m) . Remningstorp features a hemiboreal forest in southern Sweden, with flat topography and where 32 circular plots have been sampled between 2010 and 2011 (area: 0.5 ha; biomass: 42–242 t/ha; height: 14–32 m) . Krycklan features a boreal forest in northern Sweden, 720-km north–northeast from Remningstorp, with significant topography and where 31 stands have been sampled in 2008 (area: 2.4–26.3 ha; biomass: 23–183 t/ha; height: 7–21 m) . A high-resolution digital terrain model has been used as ground reference during InSAR processing. For the aforementioned plots and stands and if the same acquisition is used for model training and validation, the new model explains 65%–89% of the observed variance, with root-mean-square error (RMSE) of 12%–19% (median: 15%) . By fixing two of the three model parameters, accurate biomass estimation can also be done when different acquisitions or different test sites are used for model training and validation, with RMSE of 12%–56% (median: 17%) . Compared with a simple scaling model computing biomass from the phase center elevation above ground, the proposed model shows significantly better- performance in Remningstorp, as it accounts for the large canopy density variations caused by active management. In Krycklan, the two models show similar performance.

Description: Snow is important to the ecological and climate systems; however, current snowfall and snow depth in situ observations are only available sparsely on the globe. By making use of the networks of Global Positioning System (GPS) stations established for geodetic applications, it is possible to monitor snow distribution on a global scale in an inexpensive way. In this paper, we propose a new snow depth estimation approach using a geodetic GPS station, multipath reflectometry and a linear combination of phase measurements of GPS triple-frequency (L1, L2, and L5) signals. This phase combination is geometry free and is not affected by ionospheric delays. Analytical linear models are first established to describe the relationship between antenna height and spectral peak frequency of combined phase time series, which are calculated based on theoretical formulas. When estimating snow depth in real time, the spectral peak frequency of the phase measurements is obtained, and then the model is used to determine snow depth. Two experimental data sets recorded in two different environments were used to test the proposed method. The results demonstrate that the proposed method shows an improvement with respect to existing methods on average.

Description: The light rain (less than or equal to a few mm hr -1 ) is critical to the Earth's ecosystem due to the high occurrence rate, particularly in middle and high latitude (over 80%). However, it is challenging to use rainfall gauge to measure light rain due to the sampling time and bucket volume resolution. Dual-polarization radar has become an important tool for quantitative precipitation estimation because of its relatively large covering area and ability to fill the sampling void. This paper presents the application of Ku-band dual-polarization radar for light rainfall estimation. The Ku-band radar rainfall algorithms and their error structure are described. The Ku-band observations from the National Aeronautics and Space Administration (NASA) Dual-frequency Dual-polarization Doppler Radar (D3R) during the NASA Iowa Flood Studies (IFoodS) field campaign are used to derive the rainfall products. The comparisons are performed between radar rainfall estimates and ground rainfall measurements from rain gauge and Autonomous Parsivel Unit (APU). It is shown here that the radar rainfall measurements at different timescales (i.e., 5, 10, and 15 min) agree with the APU observations very well. The normalized difference error is about 26.1%, 24.8%, and 23.7%, for 5-min, 10-min, and 15-min rainfall accumulations, respectively. The excellent performance of Ku-band rainfall algorithm for light rain estimation indicates the great potential of using D3R as a ground validation tool for the Global Precipitation Measurement (GPM) satellite precipitation product evaluations.

Description: Asymmetric features of oceanic brightness temperature from spaceborne microwave imagers in high surface wind speed conditions were investigated with two kinds of collocated data. The first is simultaneous measurements of microwave brightness temperatures and surface wind vectors from the Advanced Microwave Scanning Radiometer (AMSR) and SeaWinds on Advanced Earth Observing Satellite II. The second is microwave brightness temperature observations (AMSR2 and the Special Sensor Microwave Imager Sounder) and surface wind vectors in the European Centre for Medium-Range Weather Forecasts numerical weather prediction model. Both collocated data sets showed that the vertical-polarized and the horizontal-polarized microwave brightness temperature have out-of-phase asymmetric features in terms of relative wind direction (RWD) at high surface wind speeds. Furthermore, different asymmetric features were found for the northern and Southern Hemispheres and for ascending and descending satellite orbits. Although similar asymmetric features can be found in other microwave imager studies, the causes of the asymmetry have not been fully investigated. To investigate the cause of the asymmetry, the observation frequency regarding air–sea temperature difference was examined in upwind, downwind, and crosswind cases. Two important factors contribute to the asymmetry. First, the observations from inclined polar orbit satellites provide different samplings on atmospheric stability in terms of the RWD. Second, the oceanic microwave brightness temperatures have negative correlations with atmospheric stability at high surface wind speeds. The out-of-phase asymmetry is closely related with atmospheric stability, and it appears under a high-surface-wind-speed condition.

Description: Recently, compressive hyperspectral imaging (CHI) has received increasing interests, which can recover a large range of scenes with a small number of sensors via compressed sensing (CS) theory. However, most of the available CHI methods separate and vectorize hyperspectral cubes into spatial and spectral vectors, which will result in heavy computational and storage burden in the recovery. Moreover, the complexity of real scene makes the sparsifying difficult and thus requires more measurements to achieve accurate recovery. In this paper, these two issues are addressed, and a new CHI approach via sparse tensors and nonlinear CS (NCS) is advanced for accurate maintenance of image structure with limited number of sensors. Based on a multidimensional multiplexing (MDMP) CS scheme, the observed measurements are denoted as tensors and a nonlinear sparse tensor coding is adopted, to develop a new tensor-NCS (T-NCS) algorithm for noniterative recovery of hyperspectral images. Moreover, two recovery schemes are advanced for T-NCS, including example-aided and self-learning CHI approaches. Finally, some experiments are performed on three real hyperspectral data sets to investigate the performance of T-NCS, and the results demonstrate its efficiency and superiority to the counterparts.

Description: Global or regional environmental change is of wide concern. Extensive studies have indicated that long-term vegetation cover change is one of the most important factors reflecting environmental change, and normalized difference vegetation index (NDVI) time-series data sets have been widely used in vegetation dynamic change monitoring. However, the significant residual effects and noise levels impede the application of NDVI time-series data in environmental change research. This study develops a novel and robust filter method, i.e., the moving weighted harmonic analysis (MWHA) method, which incorporates a moving support domain to assign the weights for all the points, making the determination of the frequency number much easier. Additionally, a four-step process flow is designed to make the data approach the upper NDVI envelope, so that the actual change in the vegetation can be detected. A total of 487 test pixels selected from SPOT VEGETATION 10-day MVC NDVI time-series data from January 1999 to December 2001 were used to illustrate the effectiveness of the new method by comparing the MWHA results with the results of another four existing methods. Finally, the long-term SPOT VEGETATION 10-day maximum-value compositing (MVC) NDVI time series for China from April 1998 to May 2014 was reconstructed by the use of the proposed method, and a test region in China was utilized to validate the effectiveness of the proposed MWHA method. All the results indicate that the reconstructed high-quality NDVI time series fits the actual growth profile of the vegetation and is suitable for use in further remote sensing applications.

Description: Automatic optical-to-SAR image registration is considered as a challenging problem because of the inconsistency of radiometric and geometric properties. Feature-based methods have proven to be effective; however, common features are difficult to extract and match, and the robustness of those methods strongly depends on feature extraction results. In this paper, a new method based on iterative line extraction and Voronoi integrated spectral point matching is developed. The core idea consists of three aspects: 1) An iterative procedure that combines line segment extraction and line intersections matching is proposed to avoid registration failure caused by poor feature extraction. 2) A multilevel strategy of coarse-to-fine registration is presented. The coarse registration aims to preserve main linear structures while reducing data redundancy, thus providing robust feature matching results for fine registration. 3) Voronoi diagram is introduced into spectral point matching to further enhance the matching accuracy between two sets of line intersection. Experimental results show that the proposed method improves the matching performance. Compared with previous methods, the proposed algorithm can effectively and robustly generate sufficient reliable point pairs and provide accurate registration.

Description: A methodology to retrieve soil moisture (SM) from Soil Moisture and Ocean Salinity (SMOS) data is presented. The method uses a neural network (NN) to find the statistical relationship linking the input data to a reference SM data set. The input data are composed of passive microwaves (L-band SMOS brightness temperatures, $T_{b} $ 's) complemented with active microwaves (C-band Advanced Scatterometer (ASCAT) backscattering coefficients), and Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) . The reference SM data used to train the NN are the European Centre For Medium-Range Weather Forecasts model predictions. The best configuration of SMOS data to retrieve SM using an NN is using $T_{b} $ 's measured with both H and V polarizations for incidence angles from 25° to 60°. The inversion of SM can be improved by ∼10% by adding MODIS NDVI and ASCAT backscattering data and by an additional ∼5% by using local information on the maximum and minimum records of SMOS Tb's (or ASCAT backscattering coefficients) and the associated SM values. The NN-inverted SM is able to capture the temporal and spatial variability of the SM reference data set. The temporal variability is better captured when either adding active microwaves or using a local normalization of SMOS Tb's. The NN SM products have been evaluated against in situ measurements, giving results of comparable or better (for some NN configurations) quality to other SM products. The NN used in this paper allows to retrieve SM globally on a daily basis. These results open interesting perspectives such as a near-real-time processor and data assimilation in weather prediction models.

Description: Accurate noise-power estimation in dual-polarization weather radar is necessary for the correction of all power-based moments in low signal-to-noise ratio environments. A method of real-time noise-power estimation for dual-polarization radar systems using copolar correlation and receiver power is introduced. This real-time noise-power estimation algorithm is implemented in the NASA dual-frequency dual-polarization Doppler radar, and the performance results are presented.

Description: The availability of hyperspectral images with improved spectral and spatial resolutions provides the opportunity to obtain accurate land-cover classification. In this paper, a novel methodology that combines spectral and spatial information for supervised hyperspectral image classification is proposed. A feature reduction strategy based on independent component analysis is the main core of the spectral analysis, where the exploitation of prior information coupled to the evaluation of the reconstruction error assures the identification of the best class-informative subset of independent components. Reduced attribute profiles (APs), which are designed to address well-known issues related to information redundancy that affect the common morphological APs, are then employed for the modeling and fusion of the contextual information. Four real hyperspectral data sets, which are characterized by different spectral and spatial resolutions with a variety of scene typologies (urban, agriculture areas), have been used for assessing the accuracy and generalization capabilities of the proposed methodology. The obtained results demonstrate the classification effectiveness of the proposed approach in all different scene typologies, with respect to other state-of-the-art techniques.

Description: Presents correctons made to the paper, "Extracting man-made objects from high spatial resolution remote sensing images via fast level set evolutions" (Li, Z., et al., IEEE Trans. Geosci. Remote Sens., vol. 53, no. 2, pp. 883???899, Feb. 2015).

Description: The SuperSAR imaging system, a novel multi-azimuth synthetic aperture radar (SAR) system capable of detecting Earth surface deformation in three dimensions from a single satellite platform, has recently been proposed. In this paper, we investigate the feasibility of detecting precise 3-D surface displacement measurements with the SuperSAR imaging system using a point target simulation. From this simulation, we establish both a relationship between the interferometric SAR phase and the across-track displacement and a relationship between the multiple-aperture interferometry phase and the along-track displacement based on the SuperSAR imaging geometry. The theoretical uncertainties of the SuperSAR measurement are analyzed in the across- and along-track directions, and the theoretical accuracy of the 3-D displacement measurement from the SuperSAR system is also investigated according to both the decorrelation and the squint and look angles. In the case that the interferometric coherence is about 0.8 and that five effective looks are employed, the theoretical 2-D measurement precision values are about 3.67 and 6.35 mm in the across- and along-track directions, respectively, and the theoretical 3-D measurement precision values for 3-D displacement are about 4.05, 4.56, and 3.45 mm in the east, north, and up directions, respectively. The result of this study demonstrates that the SuperSAR imaging system is capable of measuring the 3-D surface displacement in all directions with subcentimeter precision.

Description: A new unsupervised approach for characterizing seafloor in side-looking sonar imagery is proposed. The approach is based on lacunarity, which measures the pixel-intensity variation, of through-the-sensor data. No training data are required, no assumptions regarding the statistical distributions of the pixels are made, and the universe of (discrete) seafloor types need not be enumerated or known. It is shown how lacunarity can be computed very quickly using integral-image representations, thereby making real-time seafloor assessments on-board an autonomous underwater vehicle feasible. The promise of the approach is demonstrated on high-resolution synthetic-aperture-sonar imagery of diverse seafloor conditions measured at various geographical sites. Specifically, it is shown how lacunarity can effectively distinguish different seafloor conditions and how this fact can be exploited for target-detection performance prediction in mine-countermeasure operations.

Description: Scene classification has been proved to be an effective method for high spatial resolution (HSR) remote sensing image semantic interpretation. The probabilistic topic model (PTM) has been successfully applied to natural scenes by utilizing a single feature (e.g., the spectral feature); however, it is inadequate for HSR images due to the complex structure of the land-cover classes. Although several studies have investigated techniques that combine multiple features, the different features are usually quantized after simple concatenation (CAT-PTM). Unfortunately, due to the inadequate fusion capacity of $boldsymbol{k}$ -means clustering, the words of the visual dictionary obtained by CAT-PTM are highly correlated. In this paper, a semantic allocation level (SAL) multifeature fusion strategy based on PTM, namely, SAL-PTM (SAL-pLSA and SAL-LDA) for HSR imagery is proposed. In SAL-PTM: 1) the complementary spectral, texture, and scale-invariant-feature-transform features are effectively combined; 2) the three features are extracted and quantized separately by $boldsymbol{k}$ -means clustering, which can provide appropriate low-level feature descriptions for the semantic representations; and 3)the latent semantic allocations of the three features are captured separately by PTM, which follows the core idea of PTM-based scene classification. The probabilistic latent semantic analysis (pLSA) and latent Dirichlet allocation (LDA) models were compared to test the effect of different PTMs for HSR imagery. A U.S. Geological Survey data set and the UC Merced data set were utilized to evaluate SAL-PTM in comparison with the conventional methods. The experimental results confirmed that SAL-PTM is superior to the single-feature methods and CAT-PTM in the scene classification of HSR imagery.

Description: In contrast to the random nature of synthetic aperture radar (SAR) data, it is also possible to identify bright targets whose scattering properties scarcely vary within imaging and time. These targets are commonly named point-like scatterers and can be found in both urban and natural environments. Permanent-scatterer interferometry techniques single out stable scatterers in a stack of SAR images, which preserve their backscattering stability along time. However, this methodology may not be optimum in natural scenarios, where the temporal stability of the scattering is rather reduced, or when the number of available SAR acquisitions is significantly small. Consequently, alternative methods have come out to detect stable scatters in a single SAR image, thus reducing all constraints related to their temporal behavior. Particularly, spectral diversity techniques are exploited to detect the so-called coherent scatterers. In this paper, a new detection scheme based on the generalized likelihood ratio test approach (GLRTA) is proposed, and its performance is extensively evaluated compared with three of the traditional methods, namely, the sublook coherence approach, the sublook entropy approach, and the phase variance approach. Remarkably, the GLRTA exploits both amplitude and phase information and does not need any further averaging (apart from sublooking processing with reduced signal bandwidth). The presented analysis is conducted both theoretically and with simulated data. For all scenarios, the new detector outperforms the other methods. The obtained results are validated also on real data. Finally, the proposed GLRTA is tested over different scattering scenarios, considering three TerraSAR-X acquisitions.

Description: There is much current interest in using multisensor airborne remote sensing to monitor the structure and biodiversity of woodlands. This paper addresses the application of nonparametric (NP) image-registration techniques to precisely align images obtained from multisensor imaging, which is critical for the successful identification of individual trees using object recognition approaches. NP image registration, in particular, the technique of optimizing an objective function, containing similarity and regularization terms, provides a flexible approach for image registration. Here, we develop a NP registration approach, in which a normalized gradient field is used to quantify similarity, and curvature is used for regularization (NGF-Curv method). Using a survey of woodlands in southern Spain as an example, we show that NGF-Curv can be successful at fusing data sets when there is little prior knowledge about how the data sets are interrelated (i.e., in the absence of ground control points). The validity of NGF-Curv in airborne remote sensing is demonstrated by a series of experiments. We show that NGF-Curv is capable of aligning images precisely, making it a valuable component of algorithms designed to identify objects, such as trees, within multisensor data sets.

Description: The dielectric property of vegetation has a considerable effect on the characteristics of the microwave radiation of vegetation. In frozen environments, when the temperature is colder than normal, changes such as increased soluble sugar and decreased moisture content (MC) can occur in the vegetation. The dielectric property of vegetation, which is almost entirely controlled by its free and bound water content, will also change. To characterize the dielectric behavior of vegetation in frozen regions, a sensitive experiment was conducted on holly leaves with a high-performance coaxial probe over a frequency range from 0.5 to 40 GHz and a temperature range from 0 °C to −20 °C. Based on the measurements and the physical properties of the constituent substances of vegetation, a semiempirical dielectric model for holly leaves in low temperature environments was developed. In this model, a decrease in MC, which causes a reduction in the complex permittivity, was described as an increase in the ice content. The complex permittivity of bound water was measured using a saturated sucrose solution at −6.5 °C. The research will provide a reference for the dielectric property study of the vegetation in frozen environments.

Description: This paper investigates at the example of bathymetry how much an application can profit from comprehensive characterizations required for an improved calibration of data from a state-of-the-art commercial hyperspectral sensor. A NEO HySpex VNIR-1600 sensor is used for this paper, and the improvements are based on measurements of sensor properties not covered by the manufacturer, in particular, detector nonlinearity and stray light. This additional knowledge about the instrument is used to implement corrections for nonlinearity, stray light, spectral smile distortion and nonuniform spectral bandwidth and to base the radiometric calibration on a SI-traceable radiance standard. Bathymetry is retrieved from a data take from the lake Starnberg using WASI-2D. The results using the original and improved calibration procedures are compared with ground reference data, with an emphasis on the effect of stray-light correction. For our instrument, stray-light biases the detector response from 416–500 nm up to 8% and from 700–760 nm up to 5%. Stray-light-induced errors affect bathymetry mainly in water deeper than Secchi depth, whereas in shallower water, the dominant error source is the calibration accuracy of the light source used for radiometric calibration. Stray-light correction reduced the systematic error of water depth by 19% from Secchi depth to three times Secchi depth, whereas the relative standard deviation remained stable at 5%.

Description: This paper examines the multifaceted effect of the effective spatial baseline, as expressed through the vertical (interferometric) wavenumber, on the inversion of forest height from polarimetric interferometric synthetic aperture radar (Pol-InSAR) data. First, the role of the vertical wavenumber in relating forest height to the interferometric (volume) coherence is introduced. Through the review of the forest height inversion from Pol-InSAR data, the effect of the vertical wavenumber on the inversion performance is evaluated. The selection of optimum with respect to forest height inversion performance, vertical wavenumbers is discussed. The impact of the acquisition geometry and terrain slopes on the vertical wavenumber and their consideration in the inversion methodology is addressed. The individual effects discussed are demonstrated by means of airborne repeat pass Pol-InSAR acquisitions in L- and P-band acquired over different forest conditions, including a boreal, a temperate, and a tropical forest test site. The achieved forest height inversion performance is validated against reference height data derived from airborne LIDAR acquisitions.

Description: Image matching based on local invariant features is crucial for many photogrammetric and remote sensing applications such as image registration and image mosaicking. In this paper, a novel local feature descriptor named adaptive binning scale-invariant feature transform (AB-SIFT) for fully automatic remote sensing image matching that is robust to local geometric distortions is proposed. The main idea of the proposed method is an adaptive binning strategy to compute the local feature descriptor. The proposed descriptor is computed on a normalized region defined by an improved version of the prominent Hessian affine feature extraction algorithm called the uniform robust Hessian affine algorithm. Unlike common distribution-based descriptors, the proposed descriptor uses an adaptive histogram quantization strategy for both location and gradient orientations, which is robust and actually resistant to a local viewpoint distortion and extremely increases the discriminability and robustness of the final AB-SIFT descriptor. In addition to the SIFT descriptor, the proposed adaptive quantization strategy can be easily extended for other distribution-based descriptors. Experimental results on both synthetic and real image pairs show that the proposed AB-SIFT matching method is more robust and accurate than state-of-the-art methods, including the SIFT, DAISY, the gradient location and orientation histogram, the local intensity order pattern, and the binary robust invariant scale keypoint.

Description: A novel multiple-kernel learning (MKL) model is proposed for urban classification to integrate heterogeneous features (HF-MKL) from two data sources, i.e., spectral images and LiDAR data. The features include spectral, spatial, and elevation attributes of urban objects from the two data sources. With these heterogeneous features (HFs), the new MKL model is designed to carry out feature fusion that is embedded in classification. First, Gaussian kernels with different bandwidths are used to measure the similarity of samples on each feature at different scales. Then, these multiscale kernels with different features are integrated using a linear combination. In the combination, the weights of the kernels with different features are determined by finding a projection based on the maximum variance. This way, the discriminative ability of the HFs is exploited at different scales and is also integrated to generate an optimal combined kernel. Finally, the optimization of the conventional support vector machine with this kernel is performed to construct a more effective classifier. Experiments are conducted on two real data sets, and the experimental results show that the HF-MKL model achieves the best performance in terms of classification accuracies in integrating the HFs for classification when compared with several state-of-the-art algorithms.

Description: In this paper, we propose a superpixel-level sparse representation classification framework with multitask learning for hyperspectral imagery. The proposed algorithm exploits the class-level sparsity prior for multiple-feature fusion, and the correlation and distinctiveness of pixels in a spatial local region. Compared with some of the state-of-the-art hyperspectral classifiers, the superiority of the multiple-feature combination, the spatial prior utilization, and the computational complexity are maintained at the same time in the proposed method. The proposed classification algorithm was tested on three hyperspectral images. The experimental results suggest that the proposed algorithm performs better than the other sparse (collaborative) representation-based algorithms and some popular hyperspectral multiple-feature classifiers.

Description: Ground-based measurements of the hemispherical-directional reflectance factor (HDRF) of windblown snow-covered Arctic tundra were measured at large solar zenith angles ( $79^{circ}$ – $85^{circ} $ ) for six sites near the international research base in Ny-Ålesund, Svalbard. Measurements were made with the Gonio RAdiometric Spectrometer System over the viewing angles $0^{circ}$ – $50^{circ}$ and the azimuth angles $0^{circ}$ – $360^{circ} $ , for the wavelength range 400–1700 nm. The HDRF measurements showed good consistency between sites for near-nadir and backward viewing angles, with a relative standard deviation of less than 10% between sites where the snowpack was smooth and the snow depth was greater than 40 cm. The averaged HDRF showed good symmetry with respect to the solar principal plane and exhibited a forward scattering peak that was strongly wavelength dependent, with greater than a factor of 2 increase in the ratio of maximum to minimum HDRF values for all viewing angles over the wavelength range 400–1300 nm. The angular effects on the HDRF had minimal influence for viewing angles less than $15^{circ} $ in the backward viewing direction for the averaged sites and agreed well with another study of snow HDRF for infrared wavelengths, but showed differences of up to 0.24 in the HDRF for visible wavelengths owing to light-absorbing impurities measured in the snowpack. The site that had the largest roughne- s elements showed the strongest anisotropy in the HDRF, a large reduction in forward scattering, and a strong asymmetry with respect to the solar principal plane.

Description: The scattered wave is represented in terms of two independent and rotation invariant parameters: the degree of polarization (DoP) and the total scattered intensity $R_{0} $ . The scattered wave polarization signature is introduced as a convenient graphical representation of the variations of the two scattered wave observable parameters as a function of the transmitting antenna polarization. It is shown that the signature of the DoP and the total scattered intensity $(R_{0} ) $ provide information on ocean and ship scattering that is complementary to that provided by the conventional received wave intensity polarization signature. While $R_{0} $ polarization signature of the ocean's Bragg scattering is sensitive to wind speed and synthetic aperture radar illumination angle, the DoP polarization signature appears to depend mainly on ocean surface roughness. The large dynamic range and low pedestal height of ship DoP polarization signature, in comparison with that of the ocean, favors the use of DoP for ship detection in comparison with $R_{0} $ . Optimization of the scattered wave parameters appears to be a convenient tool for efficient ship detection. The scattered wave optimization technique introduced by R. Touzi et al. in the nineties, is reconsidered and applied for enhanced ship detection. It is shown that the excursion of DoP, $Delta p$ , and the minimum DoP, $p_{min} $ , permits a significant improvement in ship-sea contrast in comparison with conventional (i.e., scalar) single channel polarizations (HH, VV, and HV). Th- additional information provided by the maximum DoP, $p_{max} $ solves for ship ambiguities with land targets. Quantification of the ship-ocean contrast is reconsidered in the context of nonstationary ship signal. The ship peak signal quantification appears to be the most suitable method for accurate measurement of ship-ocean contrast in the presence of a nonstationary ship signal. The local $p_{min}$ performs better than the peak of $Delta p$ and single polarizations (HH, VV, and HV) . The added value of polarimetric RS2 information for ship detection is demonstrated using wide swath (50 km) polarimetric RADARSAT-2 data collected at 29° and 40° incidence angle over vessels (validated with Automatic Identification System data) in the Strait of Georgia, near Vancouver, Canada.

Description: In this paper, we propose an integrated framework of the recently proposed Genetic Sequential Image Segmentation (GeneSIS) algorithm. GeneSIS segments the image in an iterative manner, whereby at each iteration, a single object is extracted via a genetic algorithm-based object extraction method. This module evaluates the fuzzy content of candidate regions, and through an effective fitness function design provides objects with optimal balance between fuzzy coverage, consistency and smoothness. GeneSIS exhibits a number of interesting properties, such as reduced over-/undersegmentation, adaptive search scale, and region-based search. To enhance the capabilities of GeneSIS, we incorporate here several improvements of our initial proposal. On one hand, two modifications are introduced pertaining to the object extraction algorithm. Specifically, we consider a more flexible representation of the structural elements used for the object's extraction. Furthermore, in view of its importance, the consistency criterion is redefined, thus providing a better handling of the ambiguous areas of the image. On the other hand we incorporate three tools properly devised, according to the fuzzy principles characterizing GeneSIS. First, we develop a marker selection strategy that creates reliable markers, particularly when dealing with ambiguous components of the image. Furthermore, using GeneSIS as the essential part, we consider a generalized experimental setup embracing two different classification schemes for remote sensing images: the spectral-spatial classification and the supervised segmentation methods. Finally, exploiting the inherent property of GeneSIS to produce multiple segmentations, we propose a segmentation fusion scheme. The effectiveness of the proposed methodology is validated after thorough experimentation on four data sets.

Description: The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument aboard the Suomi National Polar-orbiting Partnership satellite has been successfully operating since its launch on October 28, 2011. Unlike its predecessor MODerate resolution Imaging Spectroradiometer (MODIS), VIIRS has no independent onboard calibrator to perform the on-orbit characterization of its spatial parameters such as the band-to-band registration (BBR), detector-to-detector registration (DDR), and modulation transfer function (MTF). The surface properties of the Moon have been demonstrated to be spectrally, radiometrically, and geometrically stable in the long term, making the Moon a suitable target for the on-orbit calibration of satelliteborne remote sensing instruments. The methodologies of spatial characterization using the Moon have been developed for MODIS and validated, through the comparison of their results to those calibrated by the onboard spatial calibrator of MODIS. In this paper, the methodologies are extended and improved for VIIRS applications. The BBR, DDR in both along-scan and along-track directions and the MTF in the along-track direction of VIIRS are calculated from the lunar observations scheduled on a nearly monthly basis. The trending results confirm that these parameters have been stable over time: the BBR offset is less than 0.05 pixels in both directions, well within the performance specification requirement of 0.1 pixels. The along-track MTF is approximately 0.6, well above the specification of 0.3, and is consistent with the prelaunch measurement. The limitation of the current methodologies and the possible future improvements are also discussed in this paper.

Description: Accurate soil moisture data, critical for many applications such as agriculture and estimation of ground water, is limited worldwide, and particularly over India, by the absence of sustained multisite observations. A long-term sustained soil moisture observation at four vertical levels (5, 15, 50, and 100 cm) is now available at several locations over India under a multi-institutional program Climate Observations and Modeling Network (COMoN) led by CSIR, India. At the same time, a high resolution $(0.1^{circ}times 0.1^{circ} )$ daily (moving 5-day mean) surface relative soil moisture data set has now become available from the Advanced Scatterometer (ASCAT) . However, there is a need to compare remotely sensed data and in situ observations to ensure consistency and quantify uncertainties. This is particularly true for India characterized by diverse climatic zones. We present a comparative analysis of gridded ASCAT soil moisture data and in situ COMoN station data over six locations in India during the period 2010–2013. A multiscale analysis is carried out involving daily, weekly, monthly, and seasonal timescales at different geographical locations. Analyses show that the two data sets are generally consistent, although there are seasonalities in the agreement; the correlation coefficient is higher for the wet season (summer, autumn), and moderate for dry season (winter, spring) . The correlation coefficients are ranged from 0.73 to 0.91 and above 99% significance level. The results quantify the reliability and robustness of ASCAT soil moisture over different climatic regions in India; the results also identify certain differences between the two data sets.

Description: The aim of this work is to characterize the seafloor by estimating invariant sand ripple parameters from synthetic aperture sonar (SAS) imagery. Using a hierarchical Bayesian framework and a known sensing geometry, a method for estimating sand ripple frequency, amplitude, and orientation values from a single SAS image, as well as from sets of SAS imagery over an area, is presented. This is accomplished through the development of an extended model for sand ripple characterization and a Metropolis-within-Gibbs sampler to estimate sand ripple frequency, amplitude, and orientation characteristics for multiaspect high-frequency side-look sonar data. Results are presented on synthetic and measured SAS imagery that indicate the ability of the proposed method to estimate desired sand ripple characteristics.

Description: In the upcoming years, many new remote sensing sensors will start operating in space. Sentinel-2 is certainly one of the most outstanding systems that will deliver a flood of detailed and continuous data from the Earth's surface during the next years. However, the heterogeneity of remote sensing data recorded using different sensors demands prelaunch activities to develop the synergies for efficient multisensor data analysis. In this context, accurate sensor simulations are a valuable tool that enables a meaningful intersensor comparison. This paper addresses the simulation of the future Sentinel-2 data and products. The presented Sentinel-2 end-to-end simulation (S2eteS) software models Sentinel-2 data acquisition, sensor calibration, and data preprocessing, which are strongly oriented on the real system. Several tests were performed to prove the software capability to generate accurate Sentinel-2 products, with regard to the quality of the radiance and reflectance products. As an example for a large variety of possible applications, the effects of unknown spectral band shifts, sensor noise, and radiometric accuracy on the accuracy of different Sentinel-2 vegetation indexes (VIs) were investigated. The software also holds the possibility to simulate other similar multispectral sensors because of its generic design.

Description: The exploitation of Doppler radars for weather observations is strongly constrained by the well-known range–velocity dilemma. To overcome the range and velocity ambiguities, dual and triple staggered pulse-repetition time (PRT) techniques are commonly used in Doppler radar systems. Today, a triple-PRT (3-PRT) scheme is operational in France. These techniques imply nonuniform sampling of the weather signal, inducing multiple replicas in the Doppler spectrum. The situation is particularly complicated for short-wavelength radars, where larger extension factors of the unambiguous Nyquist interval are needed. To overcome these difficulties, a novel technique called OptM-PRT is proposed. It mainly consists in optimizing the transmission scheme based on multiple pulse repetition time, so that the corresponding autocorrelation function is well filled. The Doppler spectrum is therefore reconstructed with much less ambiguities, from the computation of the autocorrelation function of radar signal and its Fourier transform. Considering both 3-PRT and Opt9-PRT schemes, the magnitude and Doppler velocity of radar returns in rain are simulated for different spectral widths, with and without elimination of the spectral lines of ground clutter. When the ground clutter is filtered out, the 3-PRT is found to better reproduce the Doppler velocity, whereas the Opt9-PRT better restitutes the magnitude of the signal. In the presence of noise, the Opt9-PRT scheme produces the best result for both the magnitude and velocity. The 3- and Opt9-PRT techniques have been applied to the C-band Doppler radar operating in Bourges, France. The experimental results show that Opt9-PRT efficiently reconstructs the Doppler spectrum of rain echoes.

Description: In this paper, the formulation of Stolt migration is modified for impulse borehole radar near-field imaging in the subsurface scenarios where the transceiver is widely separated with respect to the detection range. The proposed approach consists of the following aspects. First, the locations of the transmitter and receiver in the survey are regarded as independent sample dimensions, and the original sample set is converted to an enlarged virtual sample set. The frequency–wavenumber spectrum (FWS) of the virtual sample set is available via multidimensional fast Fourier transform (FFT). Then, the relation between the angular frequency and wavenumbers of the transmitter and receiver is derived in the frame of the virtual sample set, which provides the basis for the interpolation in angular frequency and weighting process of FWS. By applying multidimensional inverse FFT (IFFT) to the interpolated and weighted FWS of the virtual sample set, the energy of target responses will focus in some profile of the IFFT result, the position of which is related with the separation between the transmitter and receiver. Finally, the desired target space can be extracted from the IFFT result. The improved Stolt migration technique is compared with the conventional Stolt migration algorithm, back-projection method, and Kirchhoff migration algorithm on synthetic data and validated by single-borehole radar experiment in the subsurface scenario. The results show that the developed Stolt migration is superior to the conventional methods in cross-range resolution, computational cost, and the ability to reconstruct locations and shapes of targets.

Description: A profound and comprehensive understanding of the sensitivity of soil parameters related to backscattering coefficient is significant for the use of active microwave algorithms for soil moisture inversion. This paper presents a global sensitivity analysis (SA) based on the Advanced Integral Equation Model for soil moisture retrieval. The analysis involves diverse parameter ranges, sensor frequencies, incidence angles, surface correlation functions, and polarizations across various experiments. The primary objectives are to quantitatively and systematically evaluate the parameter sensitivities and their variations under various conditions, resulting in an improved understanding of microwave scattering and suggesting potential approaches to the improvement of soil moisture retrieval. The performance of this SA leads to the parameter sensitivities being quantified. Sensitive and insensitive parameters are distinguished. The existence of the former informs the direction of model calibration, implying that these parameters can be inverted with high confidence. Setting the latter as constants would be a step toward model simplification. Various conditions are observed to influence the parameter sensitivities, suggesting that it is possible to perform soil moisture or roughness inversions under the most sensitive conditions for the parameters. Finally, an SA of various combinations of dual-polarization, dual-frequency, and dual-incidence-angle backscatter is conducted. The results suggest that certain combinations enhance the sensitivities of certain parameters and allow for better estimation of their values. Ultimately, the presented global SA highlights the quantitative and systematic evaluation of parameter sensitivities, particularly their interactions, leading to a more profound understanding of scattering and an improvement in soil moisture estimation.

Description: Modern operational forest inventory often uses remotely sensed data that cover the whole inventory area to produce spatially explicit estimates of forest properties through statistical models. The data obtained by airborne light detection and ranging (LiDAR) correlate well with many forest inventory variables, such as the tree height, the timber volume, and the biomass. To construct an accurate model over thousands of hectares, LiDAR data must be supplemented with several hundred field sample measurements of forest inventory variables. This can be costly and time consuming. Different LiDAR-data-based and spatial-data-based sampling designs can reduce the number of field sample plots needed. However, problems arising from the features of the LiDAR data, such as a large number of predictors compared with the sample size (overfitting) or a strong correlation among predictors (multicollinearity), may decrease the accuracy and precision of the estimates and predictions. To overcome these problems, a Bayesian linear model with the singular value decomposition of predictors, combined with regularization, is proposed. The model performance in predicting different forest inventory variables is verified in ten inventory areas from two continents, where the number of field sample plots is reduced using different sampling designs. The results show that, with an appropriate field plot selection strategy and the proposed linear model, the total relative error of the predicted forest inventory variables is only 5%–15% larger using 50 field sample plots than the error of a linear model estimated with several hundred field sample plots when we sum up the error due to both the model noise variance and the model's lack of fit.

Description: The rapid development of synthetic aperture radar (SAR) sensors results in the acquisition of substantial ultrahigh-resolution SAR images. In this paper, we, for the first time, present three scenes of single-polarization SAR images with decimeter resolution obtained by a millimeter-wave (MMW) Chinese airborne SAR system. An innovative framework based on the complex generalized Gaussian distribution (CGGD) model is proposed to extract land use information from them, and three CGGD parameters, including the shape parameter, the non-Gaussianity parameter, and the noncircularity parameter, are selected to identify different kinds of ground objects. It is shown that these parameters can reveal plentiful land surface information and will be extremely helpful for single-polarization SAR image interpretations. Moreover, a decision tree classifier is built to categorize these images into homogenous natural surfaces, vegetation textures, circular man-made targets, and noncircular man-made targets. Several interesting experiments are implemented, and their results well demonstrate the effectiveness of the new framework.

Description: Surface shortwave net radiation (SSNR) is a key component of the surface radiation budget. In this paper, we refined a direct estimation approach to retrieve daily SSNR estimates from combined Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data. The retrieved MODIS SSNR estimates were validated against measurements at seven stations of the Surface Radiation Budget Network. We also compared the MODIS retrievals with three existing SSNR products: the Clouds and the Earth's Radiant Energy System (CERES) products, the North American Regional Reanalysis (NARR) data, and the ERA-Interim reanalysis data from the European Centre for Medium-Range Weather Forecasts. MODIS data at 1 km were upscaled to mitigate the mismatch between site measurements and satellite retrievals. Among the four data sets, the aggregated MODIS retrievals agreed best with in situ measurements, with a root-mean-square error (rmse) of 23.1 W/m 2 and a negative bias of 6.7 W/m 2 . The CERES products have a slightly larger rmse of 24.2 W/m 2 and a positive bias of 7.6 W/m 2 . Both reanalysis data (NARR and ERA-Interim) overestimate daily SSNR and have much larger uncertainties. Monthly satellite SSNR data are more accurate than daily values, and the scaling issue in validating monthly MODIS SSNR retrievals is also less prominent. Averaged with a window size of 23 km, the two MODIS sensors can estimate monthly SSNR with an rmse error of 11.6 W/m 2 , representing an improvement of 2.4 W/m 2 over the CERES products.

Description: Band selection is a well-known approach to reduce the dimensionality of hyperspectral imagery. Rough set theory is a paradigm to deal with uncertainty, vagueness, and incompleteness of data. Although it has been applied successfully to feature selection in different application domains, it is seldom used for the analysis of the hyperspectral imagery. In this paper, a rough-set-based supervised method is proposed to select informative bands from hyperspectral imagery. The proposed technique exploits rough set theory to compute the relevance and significance of each spectral band. Then, by defining a novel criterion, it selects the informative bands that have higher relevance and significance values. To assess the effectiveness of the proposed band selection technique, three state-of-the-art methods (one supervised and two unsupervised) used in the remote sensing literature are analyzed for comparison on three hyperspectral data sets. The results of this comparison point to the superiority of the proposed technique, especially when a small number of bands are to be selected.

Description: Processing large very high-resolution remote sensing images on resource-constrained devices is a challenging task because of the large size of these data sets. For applications such as environmental monitoring or natural resources management, complex algorithms have to be used to extract information from the images. The memory required to store the images and the data structures of such algorithms may be very high (hundreds of gigabytes) and therefore leads to unfeasibility on commonly available computers. Segmentation algorithms constitute an essential step for the extraction of objects of interest in a scene and will be the topic of the investigation in this paper. The objective of the present work is to adapt image segmentation algorithms for large amounts of data. To overcome the memory issue, large images are usually divided into smaller image tiles, which are processed independently. Region-merging algorithms do not cope well with image tiling since artifacts are present on the tile edges in the final result due to the incoherencies of the regions across the tiles. In this paper, we propose a scalable tile-based framework for region-merging algorithms to segment large images, while ensuring identical results, with respect to processing the whole image at once. We introduce the original concept of the stability margin for a tile. It allows ensuring identical results to those obtained if the whole image had been segmented without tiling. Finally, we discuss the benefits of this framework and demonstrate the scalability of this approach by applying it to real large images.

Description: An inverse scattering approach has been designed to process data gathered by means of forward-looking ground-penetrating radar systems. Such an inverse approach exploits a linear model of the scattering phenomenon and is able to account for the half-space 2-D geometry. In this frame, a theoretical study on the achievable reconstruction capabilities is provided with the aim to estimate the range and cross-range resolution limits and gain indications about the issue of how to design the measurement configuration. Finally, numerical and experimental examples are provided to assess the effectiveness of the approach.

Description: To increase the scanning rate of phased-array radar by a factor of two or more, the contiguous transmission of two or more pulses toward two or more directions and simultaneous reception of weather echoes from those directions is employed. However, if there are strong gradients of reflectivity, mainlobe-to-sidelobe coupled echoes contaminate wanted mainlobe-to-mainlobe (m–m) coupled echoes and limit the accurate analysis of weather. To retrieve m–m echoes, a technique is derived that restores wanted m–m voltages for antenna sidelobe levels as high as $-$ 30 dB and the difference in echo power of 100 dB for two simultaneously sampled beam directions. The technique is demonstrated using data collected with the WSR-88D radar and simulating PAR operations.

Description: Different Earth-observing (EO) sensors have various capabilities for diverse observing tasks. Sensor planning services make the choice of web-ready sensors for specific observing tasks with regard to observing requests and sensor capabilities. Sensor capabilities rely on various parameters; thus, choosing EO sensors for specific observing tasks relying directly on these parameters is a multicriteria decision process. A sensor's capability can be drawn from these parameters with the help of an algorithm. Furthermore, if divided into different clusters based on capabilities, applicable sensors can be more easily chosen for a category of observing tasks. In this paper, a spaceborne EO optical sensor static capability index (SSCI) mechanism is drawn from an evaluation-and-clustering algorithm, which is composed of a self-organizing neural map in combination with weighted principal component analysis. The scheme of SSCI relies on no expert analysis system and thus is more flexible and efficient. EO scenarios of disaster reactions are among the application of this algorithm. In particular, scenarios of flooding disaster forecasting, relief aiding, and postdisaster loss assessment within the framework of International Charter on Space and Major Disasters have been utilized for experiments. They have shown that the SSCI assessing algorithm is feasible and stable, and the EO optical sensor clustering algorithm based on SSCI can offer reasonable clustering accuracies of EO optical sensors. In our experiments, the EO optical sensor SSCI computation and clustering algorithm had a time consumption within 2 s and 2 min, respectively, and memory consumption within 200 MB on a normal personal computer.

Description: In blind hyperspectral unmixing (HU), the pure-pixel assumption is well known to be powerful in enabling simple and effective blind HU solutions. However, the pure-pixel assumption is not always satisfied in an exact sense, especially for scenarios where pixels are heavily mixed. In the no-pure-pixel case, a good blind HU approach to consider is the minimum volume enclosing simplex (MVES). Empirical experience has suggested that MVES algorithms can perform well without pure pixels, although it was not totally clear why this is true from a theoretical viewpoint. This paper aims to address the latter issue. We develop an analysis framework wherein the perfect endmember identifiability of MVES is studied under the noiseless case. We prove that MVES is indeed robust against lack of pure pixels, as long as the pixels do not get too heavily mixed and too asymmetrically spread. The theoretical results are supported by numerical simulation results.

Description: The eruption of the Eyjafjallajökull volcano in April–May 2010 was continuously monitored by the Keflavík C-band weather radar. The Keflavík radar is located at a distance of about 156 km from the volcano vent, and it has sensitivity of about −5 dBZ at 2-km range resolution over the volcanic area. The time series of radar volume data, which was available every 5 min, is quantitatively analyzed by using the Volcanic Ash Radar Retrieval (VARR) technique. The latter is a physically based methodology that is applied to estimate ash-fall rate and mass concentration within each radar volume. The VARR methodology is here extended, with respect to the previous formulation, to provide an approximate estimate of both mean particle diameter and airborne tephra particle size distribution under some assumptions. Deposited tephra at ground is also extrapolated together with an estimate of the magma mass flow rate (MFR) at the volcano vent, derived from the implementation of the mass continuity equation in the radar reference system. The VARR-based retrievals are compared with those derived from a direct tephra sampling at the ground, experimentally carried out in terms of ash grain size and loading during the Eyjafjallajökull eruption activity on May 5–7, 2010. VARR-based particle diameter estimates may suggest that a sorting of airborne particles during the downwind transport is taking place without observing aggregation processes during the ash fall. VARR-derived daily ash mass loadings in the period between April 14 and May 10 are also evaluated with respect to integrated ground and model-based data in the Eyjafjallajökull area. VARR-retrieved MFRs are finally compared with corresponding values obtained from analytical 1-D eruption models, using radar-estimated plume height and radio-sounding wind fields. A fairly good agreement is obtained, thus opening the exploitation of weather radar retrievals for volcanic e- uption quantitative studies and ash dispersion model initialization.

Description: A technique to estimate tropical cyclone (TC) current intensity based on geostationary satellite infrared window (IRW) and water vapor (WV) imagery is explored in this paper. First, to combine the advantages of the IRW imagery and the WV minus IRW (WV-IRW) imagery, a WV-IRW-to-IRW ratio (WIRa)-based indicator is proposed. This indicator not only can display the inner-core convection's symmetrization level and vigor but also is able to screen out thin cirrus, stratospheric WV anomaly, and overshooting tops from average deep convection. It is highly correlated with the best track minimum sea-level pressure and thus used to estimate the western North Pacific TC current intensity. Detailed analyses have demonstrated that the WIRa-based indicator can further improve the estimation of TC current intensity alongside the existing algorithms. The WIRa-based indicator is designed based on the hypothesis that “overshooting top is more useful for forecasting than initial estimation,” and the satisfying results of the WIRa-based method perhaps provide indirect evidence to support this hypothesis in turn.

Description: Since launch, the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the Terra and Aqua spacecraft have operated successfully for more than 14 and 12 years, respectively. A key instrument for National Aeronautics and Space Administration Earth Observing System missions, MODIS was designed to make continuous observations for studies of Earth's land, ocean, and atmospheric properties and to extend existing data records from heritage Earth observing sensors. The 16 thermal emissive bands (TEBs) (3.75–14.24 $mumbox{m} $ ) are calibrated on orbit using a temperature controlled blackbody (BB) . Both Terra and Aqua MODIS BBs have displayed minimal drift over the mission lifetime, and the seasonal variations of the BB temperature are extremely small in Aqua MODIS. The long-term gain and noise equivalent difference in temperature performance of the 160 TEB detectors on both MODIS instruments have been well behaved and generally very stable. Small but noticeable variations of Aqua MODIS bands 33–36 (13.34–14.24 $mumbox{m} $ ) response in recent years are primarily due to loss of temperature control margin of its passive cryoradiative cooler. As a result, fixed calibration coefficients, previously used by bands when the BB temperature is above their saturation temperatures, are replaced by the focal-plane-temperature-dependent calibration coefficients. This paper presents an overview of the MODIS TEB calibration, the on-orbit performance, and the challenging issues likely to impact the instruments as they continue operating well past their designed lifetime of six years.

Description: Phase correlation (PC) is a well-established image matching algorithm. It is robust to the variation of image contrast and brightness, but whether it is invariant to local illumination change, particularly sun angle change, is yet to be investigated. This paper presents our study to prove and demonstrate the robustness of illumination invariance of the PC algorithm via mathematical analysis and image matching experiments. First, a 3-D space named slope-aspect-intensity (SAI) is proposed to characterize the 3-D relationship between image intensity and terrain slope/aspect angles for a given illumination geometry. Based on the SAI space, the mathematical relationship between PC cross-power spectra and illumination direction (e.g., solar azimuth and zenith angles) is analyzed. The impact of illumination angle variation between two images for matching is then rigorously investigated via experiments using simulated terrain shading images from a digital elevation model and real optical images taken under different sun illumination conditions. Our study confirms that PC is robustly invariant to illumination and therefore can achieve reliable matching between images taken under different solar illumination conditions for various remote sensing applications.

Description: This paper presents a framework for a semisupervised domain adaptation method for remote sensing image classification. Most of the representation-based domain adaptation methods attempt to find a total transformation matrix for all the samples from the source domain; however, they ignore the individual changes in each class, which often leads to the misalignment of the samples in each class between the two domains. This paper attempts to find new representations for the samples in different classes from the source domain by multiple linear transformations, which corresponds to the practical changes in each class to a higher degree. Furthermore, to avoid the influence of outliers and noise in the source domain samples, low-rank reconstruction is further applied to make the domain adaptation method more robust. In addition, in the stage of predicting the unlabeled samples by label propagation (LP), the proposed LP with instance weighting can effectively further reduce the negative effect of misleading samples from the source domain. The results obtained with a QuickBird data set and a hyperspectral data set confirm the effectiveness and reliability of the proposed method.

Description: In the theoretical purview of the discrete Calculus, a rigorous gradient-based formulation of the multichannel phase unwrapping (MCh-PhU) problem is systematically established in terms of discrete differential operators, which are defined by the topology of the intrinsically discrete spaces upon which they act, thus capturing the essential topological character of the problem within a suitable matrix formalism and providing interesting implications. Within this methodological framework, the extended minimum cost flow (EMCF) algorithm, which provides an effective strategy aimed at solving the MCh-PhU problem, is revised, and its computational structure is analyzed. A parallel formulation of the computational-intensive EMCF algorithm is then presented. Emphasis is placed on the methodological and practical aspects leading to a novel dual-level parallel computational model in which the parallelism is hierarchically implemented at two different levels. Performance evaluation relevant to the implemented prototype solution is also carried out, thus quantifying the benefit of parallelism at different levels. The significant experimentally achieved speedup demonstrates the validity of our approach. As a result, the attained parallel prototype enables the large-scale solution of the MCh-PhU problem in a reasonable time frame, with a great impact on systematic exploitation of the available SAR archives.

Description: Pansharpening aims to synthesize a high-spatial-resolution multispectral (MS) image by fusing a panchromatic (PAN) image and a low-resolution MS image. The multiresolution analysis (MRA)-based methods are a popular group of pansharpening methods. However, in the MRA-based methods, spatial distortions may occur in the pansharpened product due to the misalignment of PAN and MS data. To address the spatial distortion issue in MRA-based methods, this paper proposes a two-step approach, which consists of the coarse step and the refined step. The coarse step produces a preliminary result using the traditional details injection model. Then, the preliminary product is refined with a second details injection operation in the refined step. Moreover, in our proposed two-step approach, a novel multiscale decomposition based on a normalized nonlocal means (NNLM) filter is developed to extract the spatial detail. Compared with the original nonlocal means filter, the designed NNLM makes the similarity measure more robust and accurate by exploiting the normalized intensity value and the mean value jointly. The experimental results on various satellite data demonstrate the superiority of the proposed pansharpening scheme by comparing with ten well-known methods.

Description: This paper analyzes the multiangle imaging results for bistatic synthetic aperture radar (BSAR) based on global navigation satellite systems (GNSS-BSAR). Due to the shortcoming of GNSS-BSAR images, a multiangle observation and data processing strategy based on BeiDou-2 navigation satellites was put forward to improve the quality of images and the value of system application. Twenty-six BSAR experiments were conducted and analyzed in different configurations. Furthermore, a region-based fusion algorithm using region-of-interest (ROI) segmentation was proposed to generate a high-quality fusion image. Based on the fusion image, typical targets such as water area, vegetation area, and artificial targets were compared and interpreted among single/multiple-angle images. The results reveal that the multiangle imaging method was a good technique to enhance image information, which might extend the applications of GNSS-BSAR.

Description: The Moderate Resolution Imaging Spectroradiometer (MODIS) is a whisk broom scanning radiometer, which is onboard the Terra and Aqua spacecraft. Both MODIS instruments have successfully completed more than 12 years of on-orbit flight. The long-wave infrared (LWIR) photovoltaic bands (bands 27–30, 6.72–9.73 μm) on the LWIR focal plane assembly in Terra MODIS have contamination due to electronic crosstalk. In this paper, we examine Terra MODIS band 28 (7.33 μm) crosstalk effects, their impact, and mitigation. The crosstalk signal is identified and characterized using the regular lunar observations acquired by MODIS. It is evident from the derived crosstalk coefficients that the contamination was mainly from bands 27 (6.72 μm), 29 (8.55 μm), and 30 (9.73 μm). The crosstalk coefficients are generally a small positive quantity in the early to middle part of the mission with a few exceptions, and then changing directions. A linear correction algorithm is applied to both L1B calibration and retrieval to qualitatively and quantitatively assess the impact and improvements in this paper. It is shown that the crosstalk correction improved the imagery and radiometric fidelity of this band.

Description: Accurate tree-cover estimates are useful in deriving above-ground biomass density estimates from very high resolution (VHR) satellite imagery data. Numerous algorithms have been designed to perform tree-cover delineation in high-to-coarse-resolution satellite imagery, but most of them do not scale to terabytes of data, typical in these VHR data sets. In this paper, we present an automated probabilistic framework for the segmentation and classification of 1-m VHR data as obtained from the National Agriculture Imagery Program (NAIP) for deriving tree-cover estimates for the whole of Continental United States, using a high-performance computing architecture. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on conditional random field, which helps in capturing the higher order contextual dependence relations between neighboring pixels. Once the final probability maps are generated, the framework is updated and retrained by incorporating expert knowledge through the relabeling of misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates (FPRs). The tree-cover maps were generated for the state of California, which covers a total of 11 095 NAIP tiles and spans a total geographical area of 163 696 sq. miles. Our framework produced correct detection rates of around 88% for fragmented forests and 74% for urban tree-cover areas, with FPRs lower than 2% for both regions. Comparative studies with the National Land-Cover Data algorithm and the LiDAR high-resolution canopy height model showed the effectiveness of our algorithm for generating accurate high-resolution tree-cover maps.

Description: Along-track multichannel synthetic aperture radar is usually used to achieve ground moving target detection and imaging. Nevertheless, there is a design dilemma between azimuth high resolution and wide swath (HRWS). To solve this problem in HRWS mode, we introduce a virtual multichannel (VMC) scheme. For each virtual channel, the low real pulse repetition frequency (PRF) improves the ability of resolving range ambiguity for wide-swath, and the high virtual PRF improves the capability of resolving Doppler ambiguity for azimuth high resolution. For multiple virtual channels, strong ground clutter is eliminated by the joint VMC processing. Furthermore, a detailed signal model of a moving target in the virtual channel is given, and the special false-peak effect in the azimuthal image is analyzed. Moreover, we propose a novel ground moving target processing method based on the VMC scheme and the clutter suppression interferometry (CSI) technique, which is called VMC-CSI. The integration of detection, location, velocity estimation, and imaging for ground moving targets can be achieved. Accounting for the unresolved main peak and false peak for a moving target, in the VMC-CSI method, we adopt a two-step scheme to estimate the radial velocity and along-track velocity, namely, rough estimation and precise estimation. Meanwhile, considering the same interferometric phases of the main peak and the false peak, we use false peaks first for the robustness of initial azimuth location estimation and remove false peaks afterward. Numerical simulations are provided for testing the effect of the false peak and the effectiveness of VMC-CSI.