ALBERT

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.

Description: This paper presents an open-source canopy height profile (CHP) toolkit designed for processing small-footprint full-waveform LiDAR data to obtain the estimates of effective leaf area index (LAIe) and CHPs. The use of the toolkit is presented with a case study of LAIe estimation in discontinuous-canopy fruit plantations. The experiments are carried out in two study areas, namely, orange and almond plantations, with different percentages of canopy cover (48% and 40%, respectively). For comparison, two commonly used discrete-point LAIe estimation methods are also tested. The LiDAR LAIe values are first computed for each of the sites and each method as a whole, providing “apparent” site-level LAIe, which disregards the discontinuity of the plantations' canopies. Since the toolkit allows for the calculation of the study area LAIe at different spatial scales, between-tree-level clumping can be easily accounted for and is then used to illustrate the impact of the discontinuity of canopy cover on LAIe retrieval. The LiDAR LAIe estimates are therefore computed at smaller scales as a mean of LAIe in various grid-cell sizes, providing estimates of “actual” site-level LAIe. Subsequently, the LiDAR LAIe results are compared with theoretical models of “apparent” LAIe versus “actual” LAIe, based on known percent canopy cover in each site. The comparison of those models to LiDAR LAIe derived from the smallest grid-cell sizes against the estimates of LAIe for the whole site has shown that the LAIe estimates obtained from the CHP toolkit provided values that are closest to those of theoretical models.

Description: Multifrequency and multioffset ground-penetrating radar data acquisition modes are used to maximize the information content and parameter retrieval capabilities. However, they also increase the computational cost dedicated to the inversion procedure. In this paper, the impact of the number of frequencies and the multistatic configurations on the information retrieval capabilities is investigated through the response surface topographies of the objective functions. We resort to a full-wave-inversion procedure and a recently developed electromagnetic model which takes advantage of a closed-form solution of Maxwell's equations to describe the antenna–medium system. We show with numerical and laboratory experiments the possibility of reducing the number of frequencies from several hundreds to one or several tens of components without affecting the information retrieval capabilities. We also show through several scenarios that the presence of a perfect electrical conductor increases the number of frequencies required to ensure an acceptable retrieval of the subsurface properties whereas the conductivity of the first layer and the relative permittivity of the second layer do not affect it. The results highlight that information content analyses are important in order to study and optimize data acquisition and inversion procedures, and thereby the computation time.

Description: This paper addresses some important aspects for the spaceborne/stationary bistatic synthetic aperture radar (SAR) (SS-BiSAR) imaging with the transmitter, TerraSAR-X, operated in staring spotlight (ST) mode. With the large integration time reaching 7.5 s and the azimuth steering span reaching $pm 2.2^{0}$ , several significant effects occur, including troposphere delay, precision phase and time synchronization, the curved orbit effect, azimuth spectrum aliasing problem, and efficient frequency domain focusing algorithm. To circumvent the main effects, corresponding solutions are proposed, including a precise synchronization strategy with troposphere delay correction based on the direct signal from the transmitter and a modified and integrative bistatic polar format algorithm (PFA). This paper covers the theoretical development, implementation, and analysis of the SS-BiSAR PFA based on 2-D fast Gaussian gridding nonuniform fast Fourier transform with wavefront curvature correction. Furthermore, the high-resolution ST-mode SS-BiSAR image processed by the proposed algorithm is acquired, and the differences of scattering behaviors between monostatic and bistatic SAR images are analyzed in detail.

Description: The ground penetrating radar (GPR) is a popular and successful remote sensing modality that has been investigated for landmine detection. GPR offers excellent detection performance, but it is limited by a low rate of advance (ROA) due to its short sensing standoff distance. Standoff distance refers to the distance between the sensing platform and the location in front of the platform where the GPR senses the ground. Large standoff (high ROA) sensing modalities have been investigated as alternatives to the GPR, but they do not yet achieve comparable detection performance. This paper proposes a new sensor management approach, called multistate management (MSM), which combines large and short standoff sensors on the same platform in a way that leverages their respective advantages, yielding a system with better ROA and detection performance. MSM is more difficult to analyze than traditional systems because it allows sensor activity and system velocity to change over time. Therefore, a new probabilistic model based on queuing theory, called Q-MSM, is also proposed for analyzing and designing detection systems operating with MSM. Simulations were conducted using real field-collected data for a system with a large standoff forward-looking infrared camera and a GPR. The system is operated with MSM, and the results show that this leads to better ROA and detection performance than can be attained otherwise. Furthermore, the results show that Q-MSM can accurately predict the behavior of the MSM system, validating its utility for analyzing and designing such systems.

Description: The concept of simultaneous source has recently become of interest in seismic exploration, due to its efficient or economic acquisition or both. The blended data overlapped between shot records are acquired in simultaneous source acquisition. Separating the blended data and recovering the single-shot seismic signals (the recovery) are of great importance in the scenario of current workflows, which can be called seismic simultaneous source separation. In the context of general random time-dithering firing, we propose an alternative method to separate the blended data by combining patchwise dictionary learning with sparse inversion, in which the dictionary is directly learned from the measured blended data. Apart from the sparse coding used for the coefficients, an additional regularization term on the dictionary is particularly designed to remove the severe interference noise. The efficient and flexible alternating direction method of multipliers (ADMM) is used to update the dictionary in the used alternating optimization scheme. The results obtained from the synthetic and real examples reasonably suggest that the separated seismic signals by using dictionary learning are more accurate and robust compared with that using the fixed transform basis, such as the local discrete cosine transform. The learned dictionary tailors for the recovery and is similar to the local seismic waveform, which improves the sparsity of the recovery substantially and is highly advantageous for producing the promised results.

Description: This paper investigates the impact of the soil moisture distribution in the top layer on the accuracy of soil moisture retrieval by microwave remote sensing methods. We modeled soil emission at L-band by coherent and noncoherent models for the different moisture distributions in the top layer. As a result, it is found that, at high moisture gradients, the difference between average moisture within the sensing depth at L-band and the moisture retrievable from remote sensing data can be more than 20% in absolute terms. In addition, high differences between Soil Moisture and Ocean Salinity (SMOS) Level 2 data and the in situ measurements were revealed in cases of high gradients. Such high gradients may be observed during some time in the top layer of the drying soil after rainfall. These differences are significantly more than the accuracy declared by SMOS development team. We proposed a simple method that allows the assessment of the type of soil moisture profile by SMOS and Global Change Observation Mission‐Water “SHIZUKU” (GCOM-W1) satellites data. The procedure for simple processing of data of the two satellites is described. In addition, we compared the type of soil moisture profile retrieved from satellite data and the soil moisture profile found by in situ measurements.

Description: An iterative alternating direction method of multipliers (ADMM) is proposed for inverse finite-element–boundary-integral (FE–BI) problem with total variation (TV) regularization. The inverse FE–BI fits to a wide class of penetrable sensing applications, where this study specifically targets the problem of radio tomography of asteroid interior structure using orbiting spacecraft. The TV regularizer enforces sparsity on the gradient of reconstructed permittivity, which agrees well with the “piecewise constant” reality of “rocks embedded in soil” scenario and, meanwhile, addresses the inherent ill-posedness. For large-scale asteroid problems, the distributed ADMM algorithm is adapted to solve the linear TV inversion at each iteration. The 2-D inversion is validated with the Fresnel Institute measurement data. Simulated cases of asteroid internal imaging are also presented. The proposed iterative ADMM can be also applied to similar penetrable imaging applications.

Description: GNSS Reflectometry, Scatterometry and Radio Occultation aboard ISS is the mission concept under study within the European Space Agency. Its core payload consists of an interferometric GNSS-Reflectometry ocean altimeter/scatterometer which does not need to generate any clean replicas of the GNSS codes for its operation. This paper describes a new interferometric technique by which such payload could also perform radio occultation as an add-on, without requiring any additional hardware resources, like the generation of clean code replicas or a storage of them. Two possibilities are studied. The first one consists of performing the complex autocorrelation function of the received signal transmitted by a rising or setting GNSS satellite. The autocorrelation function is evaluated around time epochs that are multiples of the period of suitable codes found in the modulation of the navigation signals. Satellite discrimination has to be performed spatially, through the antenna pattern. The second possibility consists in acquiring the reference signal separately from the occultation event which, in turn, has two options depending upon the geometry at which the reference is recorded: zenithal and top of the atmosphere. The signal-to-noise ratio, the satellite discrimination, and the impact of clock errors are assessed.

Description: Geophysics experts are interested in understanding the behavior of volcanoes and forecasting possible eruptions by monitoring and detecting the increment on volcano-seismic activity, with the aim of safeguarding human lives and material losses. This paper presents an automatic volcanic event detection and classification system, which considers feature extraction and feature selection stages, to reduce the processing time toward a reliable real-time volcano early warning system (RT-VEWS). We built the proposed approach in terms of the seismicity presented in 2009 and 2010 at the Cotopaxi Volcano located in Ecuador. In the detection stage, the recordings were time segmented by using a nonoverlapping 15-s window, and in the classification stage, the detected seismic signals were 1-min long. For each detected signal conveying seismic events, a comprehensive set of statistical, temporal, spectral, and scale-domain features were compiled and extracted, aiming to separate long-period (LP) events from volcano-tectonic (VT) earthquakes. We benchmarked two commonly used types of feature selection techniques, namely, wrapper (recursive feature extraction) and embedded (cross-validation and pruning). Each technique was used within a suitable and appropriate classification algorithm, either the support vector machine (SVM) or the decision trees. The best result was obtained by using the SVM classifier, yielding up to 99% accuracy in the detection stage and 97% accuracy and sensitivity in the event classification stage. Selected features and their interpretation were consistent among different input spaces in simple terms of the spectral content of the frequency bands at 3.1 and 6.8 Hz. A comparative analysis showed that the most relevant features for automatic discrimination between LP and VT events were one in the time domain, five in the frequency domain, and nine in the scale domain. Our study provides the framework for an event classification system with high - ccuracy and reduced computational requirements, according to the orientation toward a future RT-VEWS.

Description: Classification of hyperspectral images usually suffers from high dimensionality and few reference data, which limits the performance of the pixelwise classifiers. The spectral–spatial classifiers, which integrate the spectral data and the spatial information during the classification, perform impressively in terms of the high classification accuracy and the homogeneous appearance of the classification map. In this paper, we propose a new probabilistic framework for spectral–spatial classification (PFSSC), which integrates the spectral data and the spatial information from the probabilistic point of view. Both the spectral data and the spatial information are used to estimate the per-pixel probability, which gives the likelihood that one pixel belongs to one class, respectively. The classification map can then be directly derived from the joint probability. In the proposed framework, a pixelwise probabilistic classifier can be extended as a spectral–spatial one since it can integrate spatial information easily. Furthermore, these spectral–spatial classifiers in the proposed framework are realized in an iterative way to avoid the problem caused by the limited reference data to some extent. In each iterative step, some unassigned pixels are classified by considering the pixels assigned in previous iterative steps. In this iterative process, pixels are assigned to specific labels step by step gradually. In the proposed framework, the probabilistic support vector machine (SVM) and random forest (RF) are extended to be two spectral–spatial classifiers. In short, we denote them as SVM-PFSSC and RF-PFSSC, respectively. The experimental results show that SVM-PFSSC and RF-PFSSC outperform some pixelwise and spectral–spatial classifiers.

Description: Land cover/land use (LCLU) information extraction from multitemporal sequences of remote sensing imagery is becoming increasingly important. Mixed pixels are a common problem in Landsat and MODIS images that are used widely for LCLU monitoring. Recently developed subpixel mapping (SPM) techniques can extract LCLU information at the subpixel level by dividing mixed pixels into subpixels to which hard classes are then allocated. However, SPM has rarely been studied for time-series images (TSIs). In this paper, a spatiotemporal SPM approach was proposed for SPM of TSIs. In contrast to conventional spatial dependence-based SPM methods, the proposed approach considers simultaneously spatial and temporal dependences, with the former considering the correlation of subpixel classes within each image and the latter considering the correlation of subpixel classes between images in a temporal sequence. The proposed approach was developed assuming the availability of one fine spatial resolution map which exists among the TSIs. The SPM of TSIs is formulated as a constrained optimization problem. Under the coherence constraint imposed by the coarse LCLU proportions, the objective is to maximize the spatiotemporal dependence, which is defined by blending both spatial and temporal dependences. Experiments on three data sets showed that the proposed approach can provide more accurate subpixel resolution TSIs than conventional SPM methods. The SPM results obtained from the TSIs provide an excellent opportunity for LCLU dynamic monitoring and change detection at a finer spatial resolution than the available coarse spatial resolution TSIs.

Description: The ability to classify urban objects in large urban scenes from point clouds efficiently and accurately still remains a challenging task today. A new methodology for the effective and accurate classification of terrestrial laser scanning (TLS) point clouds is presented in this paper. First, in order to efficiently obtain the complementary characteristics of each 3-D point, a set of point-based descriptors for recognizing urban point clouds is constructed. This includes the 3-D geometry captured using the spin-image descriptor computed on three different scales, the mean RGB colors of the point in the camera images, the LAB values of that mean RGB, and the normal at each 3-D point. The initial 3-D labeling of the categories in urban environments is generated by utilizing a linear support vector machine classifier on the descriptors. These initial classification results are then first globally optimized by the multilabel graph-cut approach. These results are further refined automatically by a local optimization approach based upon the object-oriented decision tree that uses weak priors among urban categories which significantly improves the final classification accuracy. The proposed method has been validated on three urban TLS point clouds, and the experimental results demonstrate that it outperforms the state-of-the-art method in classification accuracy for buildings, trees, pedestrians, and cars.

Description: Target detection in hyperspectral images (HSIs) is an active area of research; it seeks to detect objects that are small in both number and size within a scene. The proposed work presents a new methodology for target detection in HSIs by combining kurtosis, level sets, and a size-based thresholding strategy. Kurtosis is used as a preprocessing step to initially enhance the targets in an image. Then, level sets identify and mark associations of pixels with similar spectral information as candidate targets. Finally, the size-based thresholding strategy detects true targets and discards false alarms that do not fit with target dimensions set as input parameter. In addition, we propose a novel version of level sets, which is suitable for target detection tasks in HSIs. Results show that the proposed algorithm could successfully detect targets in HSIs, and it gave better performance in terms of the receiver operating characteristic curve than other techniques widely used in target detection such as orthogonal subspace projection, constrained signal detector, constrained energy minimization, adaptive cosine/coherent estimator algorithm, and generalized-likelihood ratio test.

Description: A high-pulse-repetition-frequency (PRF) radar can handle the high Doppler frequencies of clutter echoes received by a fast-moving airborne radar. However, high-PRF radar causes range ambiguity. In addition, the clutter is range dependent when the airborne radar works in a forward-looking geometry. The range ambiguity and range dependence will lead to severe performance degradation of the traditional space-time adaptive processing (STAP) methods. In this paper, a vertical frequency diverse array (FDA), which applies frequency diversity in the vertical of a planar array, is explored to circumvent the range ambiguity problem in STAP radar. A range-ambiguous clutter suppression approach is devised, which consists of vertical spatial frequency compensation and pre-STAP filtering. In the vertical-FDA radar, the vertical spatial frequency depends not only on the depression angle but also on the slant range. By using this characteristic, the range-ambiguous clutter can be separated in the vertical spatial frequency domain, and then, clutter suppression is achieved for each separated range region. As a result, both problems of range ambiguity and range dependence are solved. Simulation results are provided to demonstrate the effectiveness of the proposed method.

Description: Synthetic aperture radar (SAR) interferometric baseline parameters form important input for SAR interferometry. In this paper, a nonlinear error model is established for the SAR interferometric baseline and parameterized as a polynomial based on the natural nonlinearity of the orbit of a satellite. Unlike conventional models, the proposed model takes into account the nonlinear part of the baseline error. A theoretical derivation is performed based on the imaging geometry of interferometric SAR, and the results of the analysis show that the parameters of the nonlinear baseline error model can be obtained from the relationship between the orbit, the nominal baseline, the baseline error, and the residual interferogram phase. A sample data set from the Japanese Earth Resources Satellite-1 (JERS-1) L-band SAR is used to validate the proposed model, and the results indicated that the compensation of the residual interferogram phase of the test data is superior to that provided by conventional models.

Description: Leaf area index (LAI) is an important vegetation biophysical variable and has been widely used for crop growth monitoring and yield estimation, land-surface process simulation, and global change studies. Several LAI products currently exist, but most have limited temporal coverage. A long-term high-quality global LAI product is required for greatly expanded application of LAI data. In this paper, a method previously proposed was improved to generate a long time series of Global LAnd Surface Satellite (GLASS) LAI product from Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data. The GLASS LAI product has a temporal resolution of eight days and spans from 1981 to 2014. During 1981–1999, the LAI product was generated from AVHRR reflectance data and was provided in a geographic latitude/longitude projection at a spatial resolution of 0.05°. During 2000–2014, the LAI product was derived from MODIS surface-reflectance data and was provided in a sinusoidal projection at a spatial resolution of 1 km. The GLASS LAI values derived from MODIS and AVHRR reflectance data form a consistent data set at a spatial resolution of 0.05°. Comparison of the GLASS LAI product with the MODIS LAI product (MOD15) and the first version of the Geoland2 (GEOV1) LAI product indicates that the global consistency of these LAI products is generally good. However, relatively large discrepancies among these LAI products were observed in tropical forest regions, where the GEOV1 LAI values were clearly lower than the GLASS and MOD15 LAI values, particularly in January. A quantitative comparison of temporal profiles shows that the temporal smoothness of the GLASS LAI product is superior to that of the GEOV1 and MODIS LAI products. Direct validation with the mean values of high-resolution LAI maps demonstrates that the GLASS LAI values were closer to the mean values of the high-resolution LAI maps ( $text{RMSE}=0.7848$ and $R^{2}=0.8095$ ) than the GEOV1 LAI values ( $text{RMSE}=0.9084$ and $R^{2}=0.7939$ ) and the MOD15 LAI values ( $text{RMSE}=1.1173$ and $R^{2}=0.6705$ ).

Description: A novel approach called Spectral–Spatial 1-D Manifold Embedding (SS1DME) is proposed in this paper for remotely sensed hyperspectral image (HSI) classification. This novel approach is based on a generalization of the recently developed smooth ordering model, which has gathered a great interest in the image processing area. In the proposed approach, first, we employ the spectral–spatial information-based affinity metric to learn the similarity of HSI pixels, where the contextual information is encoded into the affinity metric using spatial information. In our derived model, based on the obtained affinity metric, the created multiple 1-D manifold embeddings (1DMEs) consist of several different versions of 1DME of the same set of all HSI points. Since each 1DME of the data is a 1-D sequence, a label function on the data can be obtained by applying the simple 1-D signal processing tools (such as interpolation/regression). By collecting the predicted labels from these label functions, we build a subset of the current unlabeled points, on which the labels are correctly labeled with high confidence. Next, we add a proportion of the elements from this subset to the original labeled set to get the updated labeled set, which is used for the next running instance. Repeating this process for several loops, we get an extended labeled set, where the new members are correctly labeled by the label functions with much high confidence. Finally, we utilize the extended labeled set to build the target classifier for the whole HSI pixels. In the whole process, 1DME plays the role of learning data features from the given affinity metric. With the incrementation of learning features during iteration, the proposed scheme will gradually approximate the exact labels of all sample points. The proposed scheme is experimentally demonstrated using four real HSI data sets, exhibiting promising classification performance when compared with other recently introduced - patial analysis alternatives.

Description: We evaluate the potential of troposphere models derived from ground meteorological data (pressure, temperature, and relative humidity) and Global Positioning System (GPS) data to improve InSAR measurements and models derived from them. We test this approach on an ERS-2/Envisat data set collected during a transient surface deformation episode that occurred from January to July 2005 in the San Gabriel Valley, southern California, USA. We find that the interferometric phase change observed over the corresponding period cannot be solely attributed to hydrological uplift associated with rising groundwater levels but also includes a significant contribution from differential tropospheric delay due to differing quantities of water vapor in the troposphere on the two SAR observation dates. We show that, if the tropospheric phase contribution is mistakenly interpreted as the range change associated with changes in groundwater storage, both the surface displacement and the groundwater storage coefficient may be overestimated by up to 30%. This method could be applied in real time where meteorological measurements are available near one or more GPS permanent site(s).

Description: In the booming era of high-resolution synthetic aperture radar (SAR) technology, SAR advanced information retrieval is critical for effective utilization of huge-volume SAR data. One important aspect of high-resolution SAR interpretation is to explore the anisotropic and dispersive information embedded among subaperture and subband SAR images. This paper formulates the polarimetric subaperture analysis as a singular-value decomposition problem, where polarimetric and anisotropic features can be simultaneously decomposed. The decomposed singular values and left singular vectors are equivalent to eigenanalysis-based polarimetric target decomposition, whereas the right singular vectors give the corresponding anisotropic feature vectors. A physics-based parameterization is proposed for anisotropic patterns, where two anisotropic entropy parameters, namely, compactness and directivity, are proposed. Both simulation results and real SAR image analyses demonstrate that these proposed anisotropic entropies can effectively identify specific types of scatterers depending on their geometric scale, curvature, and form of spatial distribution. The proposed anisotropic entropies could be applied to single- and dual-polarization high-resolution SAR data as well.

Description: This paper presents a method for estimating the solar transmittance of urban trees using airborne light detection and ranging (LiDAR), and the radiative transfer simulation of vegetation. The leaf area density (LAD) distribution of trees with voxel size $1 text{m}times 1 text{m}times 0.5 text{m}$ is estimated using high-resolution and multireturn airborne LiDAR data. The LAD of voxels having few incident laser beams is corrected from the surrounding voxels. The LAD of the periphery of the crown is discretized into $0.5 text{m}times 0.5 text{m}times 0.5 text{m}$ voxels to accurately calculate the shaded area. The resulting LAD distribution is used in a radiative transfer simulation to calculate the solar transmittance of the trees. We verified the accuracy of the calculated transmittance by comparing it with empirical data for a Zelkova serrata . The comparisons were conducted under different angles of incidence of laser beams and solar radiation. When the angle between the incident laser beams and solar radiation was small, the transmittance could be accurately estimated. The LAD correction enabled the method to be applied to a broader range of the angle between beams and solar radiation. When the zenith angle of the incident laser beams was small $(< 10^{circ})$ and the LAD correction was carried out, the errors in transmittance were within 0.06 for solar altitudes greater than 40°. Next, we examined the difference in solar transmittance among streets caused by the layout of trees and buildings and the growth condition of the trees. It was shown that the present method is able to quantify the solar shading provided by urban trees and take into account LAD, tree layout, and the spatial geometry of the surr- unding buildings.

Description: This paper investigates several optimum graph-cut techniques for pruning binary partition trees (BPTs) and their usefulness for the low-level processing of polarimetric synthetic aperture radar (PolSAR) images. BPTs group pixels to form homogeneous regions, which are hierarchically structured by inclusion in a binary tree. They provide multiple resolutions of description and easy access to subsets of regions. Once constructed, BPTs can be used for a large number of applications. Many of these applications consist in populating the tree with a specific feature and in applying a graph cut called pruning to extract a partition of the space. In this paper, different pruning examples involving the optimization of a global criterion are discussed and analyzed in the context of PolSAR images for segmentation. Through the objective evaluation of the resulting partitions by means of precision-and-recall-for-boundaries curves, the best pruning technique is identified, and the influence of the tree construction on the performances is assessed.

Description: Building on existing techniques for satellite remote sensing of fires, this paper takes advantage of the day–night band (DNB) aboard the Visible Infrared Imaging Radiometer Suite (VIIRS) to develop the Firelight Detection Algorithm (FILDA), which characterizes fire pixels based on both visible-light and infrared (IR) signatures at night. By adjusting fire pixel selection criteria to include visible-light signatures, FILDA allows for significantly improved detection of pixels with smaller and/or cooler subpixel hotspots than the operational Interface Data Processing System (IDPS) algorithm. VIIRS scenes with near-coincident Advanced Spaceborne Thermal Emission and Reflection (ASTER) overpasses are examined after applying the operational VIIRS fire product algorithm and including a modified “candidate fire pixel selection” approach from FILDA that lowers the 4- $mutext{m}$ brightness temperature (BT) threshold but includes a minimum DNB radiance. FILDA is shown to be effective in detecting gas flares and characterizing fire lines during large forest fires (such as the Rim Fire in California and High Park fire in Colorado). Compared with the operational VIIRS fire algorithm for the study period, FILDA shows a large increase (up to 90%) in the number of detected fire pixels that can be verified with the finer resolution ASTER data (90 m). Part (30%) of this increase is likely due to a combined use of DNB and lower 4- $mutext{m}$ BT thresholds for fire detection in FILDA. Although further studies are needed, quantitative use of the DNB to improve fire detection could lead to reduced response times to wildfires and better estimate of fire characteristics (smoldering and flaming) at night.

Description: Hyperspectral image (HSI) denoising is a crucial preprocessing task that is used to improve the quality of images for object detection, classification, and other subsequent applications. It has been reported that noise can be effectively removed using the sparsity in the nonnoise part of the image. With the appreciable redundancy and correlation in HSIs, the denoising performance can be greatly improved if this redundancy and correlation is utilized efficiently in the denoising process. Inspired by this observation, a noise reduction method based on joint spectral–spatial distributed sparse representation is proposed for HSIs, which exploits the intraband structure and the interband correlation in the process of joint sparse representation and joint dictionary learning. In joint spectral–spatial sparse coding, the interband correlation is exploited to capture the similar structure and maintain the spectral continuity. The intraband structure is utilized to adaptively code the spatial structure differences of the different bands. Furthermore, using a joint dictionary learning algorithm, we obtain a dictionary that simultaneously describes the content of the different bands. Experiments on both synthetic and real hyperspectral data show that the proposed method can obtain better results than the other classic methods.

Description: Miniaturized hyperspectral imaging sensors are becoming available to small unmanned airborne vehicle (UAV) platforms. Imaging concepts based on frame format offer an attractive alternative to conventional hyperspectral pushbroom scanners because they enable enhanced processing and interpretation potential by allowing for acquisition of the 3-D geometry of the object and multiple object views together with the hyperspectral reflectance signatures. The objective of this investigation was to study the performance of novel visible and near-infrared (VNIR) and short-wave infrared (SWIR) hyperspectral frame cameras based on a tunable Fabry–Pérot interferometer (FPI) in measuring a 3-D digital surface model and the surface moisture of a peat production area. UAV image blocks were captured with ground sample distances (GSDs) of 15, 9.5, and 2.5 cm with the SWIR, VNIR, and consumer RGB cameras, respectively. Georeferencing showed consistent behavior, with accuracy levels better than GSD for the FPI cameras. The best accuracy in moisture estimation was obtained when using the reflectance difference of the SWIR band at 1246 nm and of the VNIR band at 859 nm, which gave a root mean square error (rmse) of 5.21 pp (pp is the mass fraction in percentage points) and a normalized rmse of 7.61%. The results are encouraging, indicating that UAV-based remote sensing could significantly improve the efficiency and environmental safety aspects of peat production.

Description: This paper analyzes the availability and accuracy of coastal altimetry sea level products in the Strait of Gibraltar. All possible repeats of two sections of the Envisat and AltiKa ground-tracks were used in the eastern and western portions of the strait. For Envisat, along-track sea level anomalies (SLAs) at 18-Hz posting rate were computed using ranges from two sources, namely, the official Sensor Geophysical Data Records (SGDRs) and the outputs of a coastal waveform retracker, the Adaptive Leading Edge Subwaveform (ALES) retracker; in addition, SLAs at 1 Hz were obtained from the Centre for Topographic studies of the Ocean and Hydrosphere (CTOH). For AltiKa, along-track SLA at 40 Hz was also computed both from SGDR and ALES ranges. The sea state bias correction was recomputed for the ALES-retracked Envisat SLA. The quality of these altimeter products was validated using two tide gauges located on the southern coast of Spain. For Envisat, the availability of data close to the coast depends crucially on the strategy followed for data screening. Most of the rejected data were due to the radar instrument operating in a low-precision nonocean mode. We observed an improvement of about 20% in the accuracy of the Envisat SLAs from ALES compared to the standard (SGDR) and the reprocessed CTOH data sets. AltiKa shows higher accuracy, with no significant differences between SGDR and ALES. The use of products from both missions allows longer times series, leading to a better understanding of the hydrodynamic processes in the study area.

Description: This paper describes the analysis performed on coherent simultaneously recorded monostatic and bistatic sea clutter data. The data were generated using a networked pulsed radar system, namely, NetRAD. This analysis is completed in both the temporal and Doppler domains, and the parameters characterized are compared between multiple bistatic angles and different polarizations. The K-distribution model is used to assess the variation in the clutter amplitude statistics between multiple bistatic data and the corresponding monostatic data. Key characteristics of the Doppler data, such as the spectrum width, center of gravity (CoG), and variance of the spectral width, are evaluated as a function of bistatic angle allowing novel relationships to be defined. The results conclude that the bistatic Doppler data have a lower K-distribution shape parameter in the majority of bistatic angles compared with the simultaneous monostatic data. In addition, novel trends in the relationship between the clutter spectrum CoG and the clutter intensity are presented.

Description: Icebergs represent hazards to maritime traffic and offshore operations. Satellite synthetic aperture radar (SAR) is very valuable for the observation of polar regions, and extensive work was already carried out on detection and tracking of large icebergs. However, the identification of small icebergs is still challenging especially when these are embedded in sea ice. In this paper, a new detector is proposed based on incoherent dual-polarization SAR images. The algorithm considers the limited extension of small icebergs, which are supposed to have a stronger cross-polarization and higher cross- over copolarization ratio compared to the surrounding sea or sea ice background. The new detector is tested with two satellite systems. First, RADARSAT-2 quad-polarimetric images are analyzed to evaluate the effects of high-resolution data. Subsequently, a more exhaustive analysis is carried out using dual-polarization ground-detected Sentinel-1a extra wide swath images acquired over the time span of two months. The test areas are in the east coast of Greenland, where several icebergs have been observed. A quantitative analysis and a comparison with a detector using only the cross-polarization channel are carried out, exploiting grounded icebergs as test targets. The proposed methodology improves the contrast between icebergs and sea ice clutter by up to 75 times. This returns an improved probability of detection.

Description: A novel wavelet-based scheme to increase coefficient independence in hyperspectral images is introduced for lossless coding. The proposed regression wavelet analysis (RWA) uses multivariate regression to exploit the relationships among wavelet-transformed components. It builds on our previous nonlinear schemes that estimate each coefficient from neighbor coefficients. Specifically, RWA performs a pyramidal estimation in the wavelet domain, thus reducing the statistical relations in the residuals and the energy of the representation compared to existing wavelet-based schemes. We propose three regression models to address the issues concerning estimation accuracy, component scalability, and computational complexity. Other suitable regression models could be devised for other goals. RWA is invertible, it allows a reversible integer implementation, and it does not expand the dynamic range. Experimental results over a wide range of sensors, such as AVIRIS, Hyperion, and Infrared Atmospheric Sounding Interferometer, suggest that RWA outperforms not only principal component analysis and wavelets but also the best and most recent coding standard in remote sensing, CCSDS-123.

Description: Automatic target generation process (ATGP) has been widely used for unsupervised target detection. However, as designed, it detects targets using full-band information. Unfortunately, on many occasions, various targets can be detected using varying bands, and ATGP can only provide one-shot target detection with all bands being used. This paper develops a new approach which can implement ATGP bandwise in a progressive manner, called progressive band processing of ATGP (PBP-ATGP) so that ATGP can be carried out band by band. Since PBP-ATGP must repeatedly implement orthogonal projections, recursive equations are further derived for PBP-ATGP, to be called recursive band processing of ATGP (RBP-ATGP) which can implement PBP-ATGP recursively. As a result, many advantages can be benefited from RBP-ATGP. Most importantly, RBP-ATGP can generate 3-D interband progressive profiles from band to band that can be used for progressive target detection, a task for which no target detection techniques using full-band information can provide.

Description: With the steadily increasing spatial resolution of synthetic aperture radar images, the need for a consistent but locally adaptive image enhancement rises considerably. Numerous studies already showed that adaptive multilooking, able to adjust the degree of smoothing locally to the size of the targets, is superior to uniform multilooking. This study introduces a novel approach of multiscale and multidirectional multilooking based on intensity images exclusively but applicable to an arbitrary number of image layers. A set of 2-D circular and elliptical filter kernels in different scales and orientations (named Schmittlets) is derived from hyperbolic functions. The original intensity image is transformed into the Schmittlet coefficient domain where each coefficient measures the existence of Schmittlet-like structures in the image. By estimating their significance via the perturbation-based noise model, the best-fitting Schmittlets are selected for image reconstruction. On the one hand, the index image indicating the locally best-fitting Schmittlets is utilized to consistently enhance further image layers, e.g., multipolarized, multitemporal, or multifrequency layers, and on the other hand, it provides an optimal description of spatial patterns valuable for further image analysis. The final validation proves the advantages of the Schmittlets over six contemporary speckle reduction techniques in six different categories (preservation of the mean intensity, equivalent number of looks, and preservation of edges and local curvature both in strength and in direction) by the help of four test sites on three resolution levels. The additional value of the Schmittlet index layer for automated image interpretation, although obvious, still is subject to further studies.

Description: The Clouds and the Earth's Radiant Energy System (CERES) instrument requires in-flight calibration and validation to maintain its accuracy during orbit operations over an extended period. An internal calibration system provides calibration for the three channels; however, there is no device for calibration of the shortwave response of the total channel. A three-channel comparison technique has been developed to calibrate the shortwave response of the total channel using the tropical oceans as a vicarious calibration target. The difference between day and night outgoing longwave radiances (OLR) averaged over the tropical oceans is used to validate the day OLR. This paper evaluates the efficacy of the technique. A relation is computed at night between the window channel radiance and the OLR retrieved from the total channel for each month for each instrument. The relation has a standard deviation of 0.28 $text{W}cdottext{m}^{-2}cdottext{sr}^{-1}$ . Given 120 months of data, the precision of the curved line faired through these data is better than 0.05 $text{W}cdottext{m}^{-2}cdottext{sr}^{-1}$ . A bias is found between FM-1 and FM-3 of 0.3 $text{W}cdottext{m}^{-2}cdottext{sr}^{-1}$ , which is taken to be the accuracy with which the total channels can be calibrated with the internal blackbodies. This result includes the differences of longwave spectral responses of the instruments. The tropical mean OLR is between 87.4 and 90.2 $text{W}cdottext{m}^{-2}cdottext{sr}^{-1}$ at night, with a standard deviation of 0.44 for FM-1 and 0.47 $text{W}cdottext{m}^{-2}cdottext{sr}^{-1}$ for FM-3. The avera- e difference between day and night tropical mean from the four instruments is $0.6pm 0.09 text{W}cdottext{m}^{-2}cdottext{sr}^{-1}$ over their data periods.

Description: The problem of supervised classification of multiresolution images, which are composed of a higher resolution panchromatic channel and of several coarser resolution multispectral channels, is addressed in this paper by proposing a novel contextual method based on Markov random fields. The method iteratively exploits a linear mixture model for the relationships between data at different resolutions and a graph cut approach to Markovian energy minimization to generate a contextual classification map at the highest resolution available in the input data set. The estimation of the parameters of the method is performed by extending recently proposed techniques based on the expectation-maximization and Ho–Kashyap's algorithms. The method is experimentally validated with semisimulated and real data involving both IKONOS and Landsat-7 ETM+ images, and the results are compared with those generated by previous approaches to the classification of multiresolution imagery.

Description: Phased array weather radars, particularly with high temporal resolution, essentially need a robust and fast beamformer to accurately estimate precipitation profiles such as reflectivity and Doppler velocity. In this paper, we introduce a neural-network-based beamformer to address this problem. In particular, the optimum weight vector is computed by modeling the problem as a three-layer radial basis function neural network (RBFNN), which is trained with I/O pairs obtained from the optimum Wiener solution. The RBFNN was chosen because of its characteristic of accurate approximation and good generalization, and its robustness against interference and noise. The proposed RBFNN beamforming method is compared with traditional beamforming methods, namely, Fourier beamforming (FR), Capon beamforming, and the flower pollination algorithm (FPA), which is a recently proposed nature-inspired optimization algorithm. It is shown that the RBFNN approach has nearly optimal performance in various precipitation radar signal simulations relative to the rival methods. The validity of the RBFNN beamformer is demonstrated by using real weather data collected by the phased array radar (PAR) at Osaka University, and compared with, in addition to the FR and FPA methods, the minimum mean square error beamforming method. It is shown that the RBFNN method estimates the reflectivity of the PAR at Osaka University with less clutter level than those of the other three methods.

Description: The detection of ground moving targets with arbitrary linear motion from an airborne multichannel radar via a long coherent processing interval is considered. The technique maximizes the target signal energy in synthetic aperture radar (SAR) images by matching to the target's linear motion profile. A reparameterization of the target's linear motion is developed that decouples the array processing from the image formation process. An explicit approach to forming SAR images focused with respect to a particular along-track velocity is presented. The ground clutter is canceled via an adaptive array technique, which also yields an estimate of the target radial velocity. Because the algorithm generates estimates of the target's along-track and cross-track velocity components, a unique determination of the target's motion parameters is possible. The approach is derived and investigated via simulated point spread functions. Algorithm performance is demonstrated on the GOTCHA SAR ground moving target indication data set.

Description: Canopy structure plays an essential role in biophysical activities in forest environments. However, quantitative descriptions of a 3-D canopy structure are extremely difficult because of the complexity and heterogeneity of forest systems. Airborne laser scanning (ALS) provides an opportunity to automatically measure a 3-D canopy structure in large areas. Compared with other point cloud technologies such as the image-based Structure from Motion, the power of ALS lies in its ability to penetrate canopies and depict subordinate trees. However, such capabilities have been poorly explored so far. In this paper, the potential of ALS-based approaches in depicting a 3-D canopy structure is explored in detail through an international benchmarking of five recently developed ALS-based individual tree detection (ITD) methods. For the first time, the results of the ITD methods are evaluated for each of four crown classes, i.e., dominant, codominant, intermediate, and suppressed trees, which provides insight toward understanding the current status of depicting a 3-D canopy structure using ITD methods, particularly with respect to their performances, potential, and challenges. This benchmarking study revealed that the canopy structure plays a considerable role in the detection accuracy of ITD methods, and its influence is even greater than that of the tree species as well as the species composition in a stand. The study also reveals the importance of utilizing the point cloud data for the detection of intermediate and suppressed trees. Different from what has been reported in previous studies, point density was found to be a highly influential factor in the performance of the methods that use point cloud data. Greater efforts should be invested in the point-based or hybrid ITD approaches to model the 3-D canopy structure and to further explore the potential of high-density and multiwavelengths ALS data.

Description: The dictionary-aided sparse regression (SR) approach has recently emerged as a promising alternative to hyperspectral unmixing in remote sensing. By using an available spectral library as a dictionary, the SR approach identifies the underlying materials in a given hyperspectral image by selecting a small subset of spectral samples in the dictionary to represent the whole image. A drawback with the current SR developments is that an actual spectral signature in the scene is often assumed to have zero mismatch with its corresponding dictionary sample, and such an assumption is considered too ideal in practice. In this paper, we tackle the spectral signature mismatch problem by proposing a dictionary-adjusted nonconvex sparsity-encouraging regression (DANSER) framework. The main idea is to incorporate dictionary-correcting variables in an SR formulation. A simple and low per-iteration complexity algorithm is tailor-designed for practical realization of DANSER. Using the same dictionary-correcting idea, we also propose a robust subspace solution for dictionary pruning. Extensive simulations and real-data experiments show that the proposed method is effective in mitigating the undesirable spectral signature mismatch effects.

Description: The Global Navigation Satellite System Occultation Sounder (GNOS) has been planned for the five Feng-Yun 3 series (FY3) weather satellites since 2013, the first of which, the FY3C satellite, was launched successfully at 03:07 UTC on September 23, 2013 from the Taiyuan Satellite Base, Shanxi province, China, into the orbit of 836-km altitude and 98.75° inclination. In addition to the Global Positioning System (GPS), the FY3C/GNOS is capable of tracking the occultation signal of the BeiDou Navigation Satellite System (BDS) (also called COMPASS) from space for the first time. The quality of BDS radio occultation (RO) has been verified in terms of signal-to-noise ratio. In this paper, the electron density profiles (EDPs) observed by FY3C/GNOS from both GPS RO and BDS RO, which were processed and archived in the National Satellite Meteorological Center of China Meteorological Administration, are compared with 32 globally distributed ionosonde observations, and then, we compare GPS RO EDPs with ionosonde observations at Mohe (52.0° N, 122.5° E), Beijing (40.3° N, 116.2° E), Wuhan (31.0° N, 114.5° E), and Sanya (18.3° N, 109.6° E). FY3C/GNOS EDPs show good agreement with ionosonde measurements, with larger discrepancies near the equatorial ionization anomaly region at Wuhan and Sanya. The ionospheric peak density (NmF2) and peak height (hmF2) derived from FY3C/GNOS EDPs are also compared with those obtained from the globally distributed ionosondes for the day of year 274–365 in 2013. In general, NmF2 and hmF2 have a higher correlation coefficient in the middle–high latitude than in the lower latitude region, due to the difference of ionospheric horizontal inhomogeneity. We also compared the NmF2 and hmF2 maps between FY3C/GNOS and the International Reference Ionosphere 2012 (IRI-2012) model. However, the wavenumber-4 structure, which can be indicated clearly from FY3C/GNOS observations, could not- be reproduced well by IRI-2012. Further investigations show that the nighttime EDPs have obvious ionization enhancement around the ionospheric E layer over the Aurora and the South Atlantic Anomaly regions due to the energetic particle precipitation indicated by the Space Environment Monitor observations onboard FY3C.

Description: Clouds often limit the ability of optical satellite sensors (such as the newly launched Gaofen-1 (GF-1) satellite in China) to observe regional soil moisture at high spatial resolutions, especially under full-cloud-contamination condition. Thus, accurate reconstruction of regional soil moisture in this case became a great methodological challenge because of the complexity and ill-posed nature of the problem. In this paper, we present a Satellite and In situ sensor Collaborated Reconstruction (SICR) method. In this method, four reconstruction rules were proposed to rebuild four kinds of corresponding missing pixels, defined as follows: C1 pixel (including one in situ sensor in its area), C2 pixel (physically similar to C1), C3 pixel (with a regular soil moisture observation sequence), and C4 pixel (remaining). By analyzing soil moisture observation relationships between these four types of pixels with in situ measurements and within these pixels, four numerical reconstruction rules were established. Linear regression, similar pixel determination, least square method, and geostatistical interpolation algorithms were used in these four rules. At last, all blank soil moisture pixels in the target soil moisture image can be filled by the SICR method. The experiment conducted in the central south of U.S. integrated 11 in situ soil moisture sensors from the United States Department of Agriculture with 11 GF-1 satellite soil moisture images. It was demonstrated that GF-1 soil moisture observations on October 17, 2014, were successfully reconstructed by the SICR method, based on the evaluations of visual appearance comparison, error distribution analysis, subimage comparison, average relative error, and universal image quality index. SICR also performed better than the reconstruction results only based on in situ or satellite sensor data. Moreover, the comparison with the soil moisture obs- rvation from the microwave sensor demonstrated the value of SICR in regional high-resolution soil moisture reconstruction. It was suggested that the SICR method provided an effective reconstruction method under full cloud contamination and showed great potential for collaborating satellite and in situ sensors.

Description: Multibaseline 2-D phase unwrapping (PU) is a critical step for the multibaseline synthetic aperture radar interferometry. Compared with the single-baseline PU, the multibaseline PU does not need to obey the phase continuity assumption, i.e., it is applicable to the terrain with the violent change. However, the performance of the multibaseline PU is directly related to noise level. In order to improve the noise robustness of the multibaseline PU, in this paper, we transplant the framework of the single-baseline PU into the multibaseline PU and propose a two-stage programming approach, referred to as TSPA, which makes use of the gradient information of the interferogram similar to how the conventional single-baseline PU method does. Fortunately, although the proposed method belongs to the integer programming (usually, the integer programming is an NP-hard problem which is hard to solve), the constraint of the optimization model of the TSPA method is unimodular, so it can be efficiently solved. Furthermore, interestingly, some useful and important concepts of the single-baseline PU, for example, residue and branch cut, are also transplanted into the multibaseline PU in this paper, and we discuss the potential of extending most of the representative single-baseline PU methods into the multibaseline domain as well. Finally, the experiment results show the effectiveness and noise robustness of the TSPA multibaseline PU method.

Description: The high-spatial-resolution aerosol retrieval algorithm using Chinese High-Resolution Earth Observation Satellite I (GF-1) wide-field images is developed, which retrieves the aerosol optical depth (AOD) over China for studying the impact of aerosol on climatic and environmental change. The algorithm is based on the red/blue surface reflectance correlations and the lookup table method. To reduce the enormous relative error caused by the constant surface reflectance relationship in the retrieval algorithm, the correlation is parameterized as a function of low, medium, and high values of normalized difference vegetation index (NDVI). Three linear relationships are simulated using MODIS BRDF-adjusted reflectance products (MCD43A4), and MODIS NDVI products are used to ascertain the value of NDVI. By applying the present algorithm to GF-1 images, two different aerosol cases of clear and turbid are analyzed to test the algorithm. Compared with the 10-km MODIS aerosol properties productions, the GF-1 retrieved AOD by our algorithm revealed a significant correlation coefficient with MODIS Dark Target AOD $(R=0.912)$ and Deep Blue AOD $(R=0.895)$ . Otherwise, the retrieved AOD results are found to be highly correlated with Aerosol Robotic Network (AERONET) sunphotometer observations $(R=0.931)$ . Compared with the results relying on the MODIS surface reflectance model, preliminary validation is encouraging that the method based on our updated surface reflectance assumptions successfully improved the accuracy, particularly under the clear sky background and over bright surface.

Description: Building orientation with respect to the radar look direction has a critical influence on the interpretation of multilook polarimetric synthetic aperture radar (PolSAR) data in urban areas. In this paper, its impacts on polarimetric orientation angle (POA) estimation and model-based decomposition are discussed. The discussion begins with the analysis of the general double-bounce scattering model, of which the characteristics are dependent on the electromagnetic and geometric parameters of the related dihedral structure. Then, for multilook PolSAR data, the polarimetric scattering mechanism in urban areas is modeled by two double-bounce scatterings from two orthogonal dihedral structures. From the model, the impacts of the building orientation on POA estimation can be revealed. With the increase of the building orientation, the POA difference between the two dihedral structures increases gradually, and the feasibility to estimate the building orientation via the estimated POA is reduced dramatically. Upon further analysis, we illustrate the impacts on the model-based decomposition. With the increase of the building orientation, the dominant scattering mechanism labeling technique based on the model-based decompositions will gradually become invalid. Moreover, the processing of POA compensation, which is helpful in reducing the impacts of the building orientation, also becomes invalid when the building orientation increases to a certain value. At last, three L-band data sets of San Francisco acquired by AIRSAR are used to verify the inferences. The experimental results show that, for L-band PolSAR data in urban areas, when the radar look angle is around 45, the threshold of building orientation for the validity of dominant scattering mechanism labeling is about ±3, and for the POA compensation, the threshold is about ±12.

Description: In this paper, for a multichannel synthetic aperture radar–ground moving target indication (SAR--GMTI) system, a new high-accuracy focusing and relocating method using instantaneous interferometry, i.e., carrying out interferometry operation in the azimuth time domain before focusing, is proposed. One of the key steps of this method is to perform instantaneous interferometry to get accurate equivalent cross-track velocity (ECV) estimation for cross-track motion compensation. After that, the signal from a moving target is concentrated in range, and along-track motion compensation becomes convenient. Motion compensation transforms a moving target into a stationary one; thus, the conventional SAR imaging algorithm can be applied to focus the moving target. Finally, a strategy for accurately relocating a moving target is presented. The processing results of simulated and measured data illustrate the effectiveness of the proposed method.

Description: Aircraft detection from very high resolution (VHR) remote sensing images has been drawing increasing interest in recent years due to the successful civil and military applications. However, several challenges still exist: 1) extracting the high-level features and the hierarchical feature representations of the objects is difficult; 2) manual annotation of the objects in large image sets is generally expensive and sometimes unreliable; and 3) locating objects within such a large image is difficult and time consuming. In this paper, we propose a weakly supervised learning framework based on coupled convolutional neural networks (CNNs) for aircraft detection, which can simultaneously solve these problems. We first develop a CNN-based method to extract the high-level features and the hierarchical feature representations of the objects. We then employ an iterative weakly supervised learning framework to automatically mine and augment the training data set from the original image. We propose a coupled CNN method, which combines a candidate region proposal network and a localization network to extract the proposals and simultaneously locate the aircraft, which is more efficient and accurate, even in large-scale VHR images. In the experiments, the proposed method was applied to three challenging high-resolution data sets: the Sydney International Airport data set, the Tokyo Haneda Airport data set, and the Berlin Tegel Airport data set. The extensive experimental results confirm that the proposed method can achieve a higher detection accuracy than the other methods.

Description: Previous studies have shown that thermal infrared anomalies can be detected in the crust rocks in satellite infrared images before an earthquake. However, thermal infrared remote sensing is easily affected by weather conditions because of the short wavelength of infrared radiation. In this paper, instead of infrared radiation, we focus on the microwave radiation characteristics of loaded rock. First, a microwave observation system was built to observe the loading process of rocks in an outdoor environment with a cold sky background. Then, the microwave radiation changes in the loaded granite samples during elastic deformation and fracturing stages were analyzed. The experiments yielded the following results. First, the microwave brightness temperature has a linear positive correlation with the load in the elastic deformation stage of the granite samples, and lateral pressure accelerates the changes in the microwave radiation. Second, the microwave brightness temperature usually decreases as the rock develops layered fractures but increases as the rock develops surface fractures, which significantly alter the surface morphology and roughness. The mechanisms responsible for the changes in microwave radiation during the rock deformation and fracturing processes are discussed. This study demonstrates the potential ability to use microwave-sensing satellites to observe seismogenic processes and earthquakes.

Description: Displacements of the Earth's surface can be estimated using differential interferometric synthetic aperture radar. The estimates are derived from the phase difference between two radar acquisitions. When at least three such acquisitions are available, one can compute the displacement between the first and the third acquisition and compare it with the sum of the two intermediate displacements. These two are expected to be equal for a piston-like spatially uniform deformation. However, this is not necessarily the case in measured data. Such lack of phase closure can be due to decorrelation noise alone. It has also been attributed to complex scattering processes such as soil moisture changes or multiple scattering sources. However, the nature of these nonrandom effects is only poorly understood in cold regions, as the role of snow and freeze/thaw processes has not been studied to date. To distinguish the noise-like and the systematic effects, an asymptotic Wald significance test is proposed. It detects situations when the observed closure error cannot solely be explained by noise. Such situations with $p < 0.05$ are observed at the Ku-band during snow metamorphism and melt and following a summer precipitation event in Sodankylä, Finland. They can also be prevalent (> 25%) in the X-band observations of ice-rich permafrost regions in the Lena Delta, Russia, indicating the presence of processes that can have systematic and deleterious impacts on the estimation of surface movements. Satellite-based monitoring of these displacements is thus possibly subject to complex error sources in high-latitude regions.

Description: In this paper, a new approach based on two fusion schemes is proposed to overcome the uncertainties in land surface emissivity (LSE) estimation and, consequently, land surface temperature (LST) retrieval. The fusion schemes are called image-based weighted methods and knowledge-based weighted methods, in which each of them includes two LSE estimation methods. The effectiveness of the two proposed fusion schemes is empirically tested over two scenes of Landsat-8 (known as Landsat Data Continuity Mission) data sets, and the obtained LSEs by individual and proposed methods were compared to the LSE product of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) by image-based and class-based cross-comparison. In both scenes, the adjusted normalized emissivity method (ANEM) and NDVI-based emissivity method (NBEM) provide appropriate results among five individual methods. In contrast, weighted to median (WMED) achieves superior results among the proposed fusion methods for both scenes. In addition, the root-mean-square error (rmse) values of LSE obtained by ANEM and WMED are 1.48% and 0.87%, which lead to 1.25 K and 0.73 K errors in the LST retrieval by the single-channel algorithm in the first scene, respectively. For the second scene, the error values of NBEM and WMED are 1.10% and 0.52%, which lead to 0.93 K and 0.44 K errors in the LST, respectively. Moreover, the error ranges and rmse of cross-comparison for the obtained LSE in the proposed methods were remarkably decreased. Also, in this research, for LST cross-comparison, an alternative scaling method based on LST products of the Moderate Resolution Imaging Spectroradiometer was proposed. The LST validation results demonstrated that the proposed methods provide better estimates in terms of three accuracy measures in both examined data sets.

Description: Television (TV) radio frequency interference (TFI) signals are found in the Advanced Microwave Scanning Radiometer 2 (AMSR2) observations of those channels with their frequencies centered at 18.7- or 10.65-GHz frequencies over coastal regions near the U.S. and Europe, respectively. When TV signals are reflected off the ocean surface and get into AMSR2 field of views, the AMSR2-measured radiance contains not only information of natural emission from Earth's surface but also the reflected TV signals. If not detected and corrected, TFI introduces errors into the geophysical retrieval products. The occurrence and intensity of TFI are determined by the angle between the observation beam vector and the reflected TV signal vector (i.e., TFI glint angle) and the background TV signal intensity. In this paper, an empirical model is developed to quantitatively calculate the contribution of TFI signals to AMSR2 observations based on TFI glint angle and TV signal intensity. This empirical model is then applied to AMSR2 K-band channels over North America and X-band channels over Europe. It is shown that the annual mean bias for the TFI-affected observations of the 18.7-GHz channel at horizontal (vertical) polarization reduces from a value of more than 5 K (2 K) to about −0.5 K (0.5) after TFI correction over the coastal ocean near North America. The annual mean bias for the TFI-affected observations of the 10.65-GHz channel at horizontal (vertical) polarization reduces from a value of about 2.5 K to about −0.7 K (0.5 K) after TFI correction over the coastal ocean near Europe. False maxima in AMSR2-retrieved cloud liquid water path and dry anomalies in AMSR2-retrieved total precipitable water near the coastal regions are also eliminated after incorporating the TFI correction.

Description: The simplex growing algorithm (SGA) has been widely used for finding endmembers. It can be considered as a sequential version of the well-known endmember finding algorithm, N-finder algorithm (N-FINDR), which finds endmembers one at a time by growing simplexes. However, one of the major hurdles for N-FINDR and SGA is the calculation of simplex volume (SV) which poses a great challenge in designing any algorithm using SV as a criterion for finding endmembers. This paper develops an orthogonal projection (OP)-based SGA (OP-SGA) which essentially resolves this computational issue. It converts the issue of calculating SV to calculating the OP on previously found simplexes without computing matrix determinants. Most importantly, a recursive Kalman filter-like OP-SGA, to be called recursive OP-SGA (ROP-SGA), can be also derived to ease computation. By virtue of ROP-SGA, several advantages and benefits in computational savings and hardware implementation can be gained for which N-FINDR and SGA do not have.

Description: In this paper, we propose a new test statistic for unsupervised change detection in polarimetric radar images. We work with multilook complex covariance matrix data, whose underlying model is assumed to be the scaled complex Wishart distribution. We use the complex-kind Hotelling–Lawley trace (HLT) statistic for measuring the similarity of two covariance matrices. The distribution of the HLT statistic is approximated by a Fisher–Snedecor distribution, which is used to define the significance level of a false alarm rate regulated change detector. Experiments on simulated and real PolSAR data sets demonstrate that the proposed change detection method gives detection rates and error rates that are comparable with the generalized likelihood ratio test.

Description: This paper deals with the exploitation of Doppler centroid measurements for ship velocity estimation from focused single-look complex synthetic aperture radar (SAR) images. An algorithm is presented, which can be used as a discrimination tool to reduce the false alarm rate of standard adaptive threshold detectors and to complement the ship detection task with velocity estimation. The outputs are the indication of the presence of a moving target and the estimate of its slant range velocity. After a review of its theoretical background, algorithm features and performance are verified by application to TerraSAR-X data. The proposed method performs robust Doppler spectrum derivation for candidate ship targets and background pixels. The presented results show that the estimated radial velocity is in very good agreement (5% root-mean-square deviation) with that resulting from the azimuth offset method. Rejection of bright sea features and azimuth ambiguities is also demonstrated, taking advantage of the performed velocity analysis.

Description: Global Positioning System (GPS) radio-occultation (RO) is an atmospheric sounding technique utilizing the received GPS signal through the stratified atmosphere to measure refractivity, which provides information on temperature and humidity. The GPS-RO technique is now operational on several Low Earth Orbiting (LEO) satellites, which cannot provide high temporal and spatial resolution soundings necessary to observe localized transient events, such as tropical storms. An airborne RO (ARO) system has thus been developed for localized GPS-RO campaigns. RO signals in the lower troposphere are adversely affected by rapid phase accelerations and severe signal power fading. These signal dynamics often cause the phase-locked loop in conventional GPS survey receivers to lose lock in the lower troposphere, and the open-loop (OL) tracking in postprocessing is used to overcome this problem. OL tracking also allows robust processing of rising GPS signals, approximately doubling the number of observed occultations. An approach for “backward” OL tracking was developed, in which the correlations are computed sequentially in reverse time so that the signal can be acquired and tracked at high elevations for rising occultations. Ultimately, the signal-to-noise ratio (SNR) limits the depth of tracking in the atmosphere. We have developed a model relating the SNR to the variance in the residual phase of the observed signal produced from OL tracking. In this paper, we demonstrate the applicability of the phase variance model to airborne data. We then apply this model to set a threshold on refractivity retrieval based upon the cumulative unwrapping error bias to determine the altitude limit for reliable signal tracking. We also show consistency between the ARO SNR and collocated COSMIC satellite observations and use these results to evaluate the antenna requirements for an improved ARO system.

Description: The imagery of highly squinted synthetic aperture radar mounted on maneuvering platforms with nonlinear trajectory is a challenging task due to the existence of acceleration and the cross-range-dependent range migration and Doppler parameters. In order to accommodate these issues, a frequency-domain imaging algorithm based on tandem two-step nonlinear chirp scaling (TNCS) with small aperture is proposed. For the cross-range-dependent range cell migration (RCM) caused by the linear range walk correction and acceleration, the first-step NCS is introduced to suppress this dependence and realize the unified RCM correction. Based on the differences between full-aperture and small-aperture data in the cross-range processing, the second-step NCS is introduced in frequency domain to equalize the cross-range-dependent Doppler parameters, for cross-range processing is more sensitive to the cross-range dependence than range processing. Furthermore, a novel geometric correction method based on inverse projection is utilized to eliminate the negative effects caused by the imaging processing. Simulation results and real data processing are presented to validate the proposed approach.

Description: We developed a large-area preprocessing framework for multisensor Landsat data, capable of processing large data volumes. Cloud and cloud shadow detection is performed by a modified Fmask code. Surface reflectance is inferred from Tanré's formulation of the radiative transfer, including adjacency effect correction. A precompiled MODIS water vapor database provides daily or climatological fallback estimates. Aerosol optical depth (AOD) is estimated over dark objects (DOs) that are identified in a combined database and image-based approach, where information on their temporal persistency is utilized. AOD is inferred with consideration of the actual target reflectance and background contamination effect. In case of absent DOs in bright scenes, a fallback approach with a modeled AOD climatology is used instead. Topographic normalization is performed by a modified C-correction. The data are projected into a single coordinate system and are organized in a gridded data structure for simplified pixel-based access. We based the assessment of the produced data set on an exhaustive analysis of overlapping pixels: 98.8% of the redundant overlaps are in the range of the expected ±2.5% overall radiometric algorithm accuracy. AOD is in very good agreement with Aerosol Robotic Network sunphotometer data ( $R^{2}$ : 0.72 to 0.79, low intercepts, and slopes near unity). The uncertainty in using the water vapor fallback climatology is approximately ±2.8% for the TM SWIR1 band in the wet season. The topographic correction was considered successful by an investigation of the nonrelationship between the illumination angle and the corrected radiance.

Description: In this paper, a transform-domain filtering method is proposed for polarimetric synthetic aperture radar (POLSAR) images via patch ordering and simultaneous sparse coding (SSC). First of all, we establish a signal-dependent additive noise model for the POLSAR covariance matrix and derive the noise variance for each element of the matrix based on the complex Wishart distribution. Next, we propose an extended patch ordering algorithm for POLSAR images by extracting sliding patches and organizing them in a regular way. Then, the ordered patches are filtered by SSC, for the purpose of which we develop a new weighted simultaneous orthogonal matching pursuit algorithm by embedding the signal-dependent noise model of the POLSAR data. Finally, the filtering result is reconstructed from the filtered patches via inverse permutation and subimage averaging. Experimental results with both simulated and real POLSAR images demonstrate that the proposed method can achieve state-of-the-art filtering performance.

Description: Remotely sensed images with very high spatial resolution provide a detailed representation of the surveyed scene with a geometrical resolution that, at the present, can be up to 30 cm (WorldView-3). A set of powerful image processing operators have been defined in the mathematical morphology framework. Among those, connected operators [e.g., attribute filters (AFs)] have proven their effectiveness in processing very high resolution images. AFs are based on attributes which can be efficiently implemented on tree-based image representations. In this paper, we considered the definition of $min$ , $max$ , $text{direct}$ , and $text{subtractive}$ filter rules for the computation of AFs over the tree-of-shapes representation. We study their performance on the classification of remotely sensed images. We compare the classification results over the tree of shapes with the results obtained when the same rules are applied on the component trees. The random forest is used as a baseline classifier, and the experiments are conducted using multispectral data sets acquired by QuickBird and IKONOS sensors over urban areas.

Description: In synthetic aperture radar (SAR) images, moving targets are usually smeared and/or imaged at incorrect positions due to the target motions during the SAR integration time. Moreover, since a high-resolution wide-swath SAR system is operated with a rather low pulse repetition frequency, a moving target will cause multiple ghost targets in the reconstructed SAR image. A new space–time adaptive processing framework is proposed in this paper for removing moving target artifacts in SAR images. In this new framework, the dynamic steering vector concept is proposed. In addition, this paper develops a moving target processing scheme for clutter suppression and moving target imaging and location for a high-resolution wide-swath SAR system. Finally, we locate the well-focused moving targets at the stationary scene image without any disturbing artifacts. The simulated and real data are used to validate the effectiveness of our proposed method.

Description: The multiplicative model, expressed as a product between the square root of a scalar positive quantity (texture) and the description of an equivalent homogeneous surface (speckle), is one of the most appropriate and disseminated models used to describe high-resolution polarimetric synthetic aperture radar (PolSAR) clutter. Generally, the texture is assumed polarization independent, which causes PolSAR data to present a spherical symmetry property, allowing for the usage of most of the algorithms present in the literature. Nevertheless, the existence of polarization-dependent clutter has also been reported, for which specific algorithms need to be derived. Therefore, it becomes clear that the first step in SAR data analysis should be the validation of the model employed. Within this context, this paper presents a new methodological framework to assess the conformity of multivariate high-resolution SAR data with respect to the product model in terms of asymptotic statistics. More precisely, spherical symmetry is investigated by applying statistical hypothesis testing on the structure of the quadricovariance matrix. Simulated data, data from the P-band airborne data set acquired by the Office National d'Études et de Recherches Aérospatiales (ONERA) over the French Guiana in 2009 in the frame of the European Space Agency campaign TropiSAR and a RAMSES X-band image acquired over Brétigny, France, are taken into consideration to investigate the performance of the derived test. The detection results are qualitatively and quantitatively analyzed, and some important conclusions are drawn regarding the methodology employed in analyzing SAR data.

Description: A synthetic aperture radar (SAR) imaging system usually produces pairs of bright area and dark area when depicting the ground objects, such as a building or tree and its shadow. Many buildings (trees) are aggregated together to form urban areas (forests). It means that the pairs of bright and dark areas often exist in the aggregated scenes. Conventional unsupervised segmentation approaches usually segment the scenes (e.g., urban areas and forests) into different regions simply according to the gray values of the image. However, a more convincing way is to regard them as the consistent regions. In this paper, we aim at addressing this issue and propose a new SAR image segmentation approach via a hierarchical visual semantic and adaptive neighborhood multinomial latent model. In this approach, the hierarchical visual semantic of SAR images is proposed, which divides SAR images into aggregated, structural, and homogeneous regions. Based on the division, different segmentation methods are chosen for these regions with different characteristics. For the aggregated region, locality-constrained linear coding-based hierarchical clustering is used for segmentation. For the structural region, visual semantic rules are designed for line object location, and a geometric structure window-based multinomial latent model is proposed for segmentation. For the homogeneous region, a multinomial latent model with adaptive window selection is proposed for segmentation. Finally, these results are integrated together to obtain the final segmentation. Experiments on both synthetic and real SAR images indicate that the proposed method achieves promising performances in terms of the consistencies of the regions and the preservations of the edges and line objects.

Description: Terrestrial laser scanning (TLS) has emerged as an effective tool for rapid comprehensive measurement of object structure. Registration of TLS data is an important prerequisite to overcome the limitations of occlusion. However, due to the high dissimilarity of point cloud data collected from disparate viewpoints in the forest environment, adequate marker-free registration approaches have not been developed. The majority of studies instead rely on the utilization of artificial tie points (e.g., reflective tooling balls) placed within a scene to aid in coordinate transformation. We present a technique for generating view-invariant feature descriptors that are intrinsic to the point cloud data and, thus, enable blind marker-free registration in forest environments. To overcome the limitation of initial pose estimation, we employ a voting method to blindly determine the optimal pairwise transformation parameters, without an a priori estimate of the initial sensor pose. To provide embedded error metrics, we developed a set theory framework in which a circular transformation is traversed between disjoint tie point subsets. This provides an upper estimate of the Root Mean Square Error (RMSE) confidence associated with each pairwise transformation. Output RMSE errors are commensurate with the RMSE of input tie points locations. Thus, while the mean output $text{RMSE}=16.3 text{cm}$ , improved results could be achieved with a more precise laser scanning system. This study 1) quantifies the RMSE of the proposed marker-free registration approach, 2) assesses the validity of embedded confidence metrics using receiver operator characteristic (ROC) curves, and 3) informs optimal sample spacing considerations for TLS data collection in New England forests. While the implications for rapid, accurate, and precise forest inventory are obvious, the conceptual framework outlined her- could potentially be extended to built environments.

Description: The product of multilook amplitudes (PMA) detector has been used to detect ships in high-resolution dual-polarization synthetic-aperture-radar images. However, the adaptive constant false-alarm rate (CFAR) technique of the PMA detector is desirable for practical applications, wherein a crucial problem is to find an appropriate model to describe the PMA statistics for varied sea surfaces. First, we consider a new probability density function to characterize the PMA statistics of homogeneous sea surfaces. Second, by using the new density and multiplicative model, the PMA detector's statistical model for nonhomogeneous sea surfaces is specified and demonstrated to be the $mathcal{G}^{0}$ distribution. Then, a theoretical analysis of the relationship between the performance of the standard CFAR detection and the parameters in the $mathcal{G}^{0}$ distribution is conducted. Experiments performed on the measured RADARSAT-2 and NASA/JPL AIRSAR images verify the effectiveness and appropriateness of the $mathcal{G}^{0}$ model for describing the statistical behavior of the PMA of sea clutter, as well as the usefulness of the model for practical ship-detection applications.

Description: Extraction of residential areas plays an important role in remote sensing image processing. Extracted results can be applied to various scenarios, including disaster assessment, urban expansion, and environmental change research. Quality residential areas extracted from a remote sensing image must meet three requirements: well-defined boundaries, uniformly highlighted residential area, and no background redundancy in residential areas. Driven by these requirements, this study proposes a global and local saliency analysis model (GLSA) for the extraction of residential areas in high-spatial-resolution remote sensing images. In the proposed model, a global saliency map based on quaternion Fourier transform (QFT) and a global saliency map based on adaptive directional enhancement lifting wavelet transform (ADE-LWT) are generated along with a local saliency map, all of which are fused into a main saliency map based on complementarities. In order to analyze the correlation among spectrums in the remote sensing image, the phase spectrum information of QFT is used on the multispectral images for producing a global saliency map. To acquire the texture and edge features of different scales and orientations, the coefficients acquired by ADE-LWT are used to construct another global saliency map. To discard redundant backgrounds, the amplitude spectrum of the Fourier transform and the spatial relations among patches are introduced into the panchromatic image to generate the local saliency map. Experimental results indicate that the GLSA model can better define the boundaries of residential areas and achieve complete residential areas than current methods. Furthermore, the GLSA model can prevent redundant backgrounds in residential areas and thus acquire more accurate residential areas.

Description: Superresolution mapping (SRM) is a technique for generating a fine-spatial-resolution land cover map from coarse-spatial-resolution fraction images estimated by soft classification. The prior model used to describe the fine-spatial-resolution land cover pattern is a key issue in SRM. Here, a novel learning-based SRM algorithm, whose prior model is learned from other available fine-spatial-resolution land cover maps, is proposed. The approach is based on the assumption that the spatial arrangement of the land cover components for mixed pixel patches with similar fractions is often similar. The proposed SRM algorithm produces a learning database that includes a large number of patch pairs for which there is a fine- and coarse-spatial-resolution representation for the same area. From the learning database, patch pairs that have similar coarse-spatial-resolution patches as those in the input fraction images are selected. Fine-spatial-resolution patches in these selected patch pairs are then used to estimate the latent fine-spatial-resolution land cover map by solving an optimization problem. The approach is illustrated by comparison against state-of-the-art SRM methods using land cover map subsets generated from the USA's National Land Cover Database. Results show that the proposed SRM algorithm better maintains the spatial pattern of land covers for a range of different landscapes. The proposed SRM algorithm has the highest overall accuracy and kappa values in all of these SRM algorithms, by using the entire maps in the accuracy assessment.

Description: Inspired by a cognitive radar in exploiting its environment to update current operation parameters and frequency diverse array (FDA) in offering a range-dependent beampattern due to the employment of frequency increments across the elements, this paper proposes a moving-target tracking approach to achieve cognitive radio frequency stealth using an FDA antenna for surveillance applications. Since surveillance systems are highly visible to intercept receivers, a traditional high-gain phased-array antenna beam is replaced by a series of low-gain FDA beams with spoiled frequency increments to reduce the system visibility, without degrading the surveillance system performance. Moreover, a cognitive closed-loop update scheme is presented to update the operation parameters in real time for improved moving-target tracking performance. All of the proposed methods are verified by simulation results.

Description: The replacement of the rational function model (RFM) by the rigorous sensor model (RSM) has been studied extensively and verified with many types of sensors and remote-sensing applications. However, relatively less research has been conducted on recovering RSM from RFM, and the few relating techniques can only be applied in specific circumstances. This paper proposes a novel linear method to obtain the position, attitude, and interior orientation (IO) elements of satellites based on the orientation information of the rays implied by the RFM. Instead of resection, forward intersection is used to solve for position, and an equivalent body coordinate system is introduced to overcome the strong correlation between the attitude and IO. The orientation information of the rays implied by the RFM is used to calculate the IO pixel by pixel. Experiments using the Ziyuan 3 panchromatic nadir sensor show that this method can recover the exterior orientation and IO elements effectively.

Description: The development of remote sensing has enabled the acquisition of information on land-cover change at different spatial scales. However, a tradeoff between spatial and temporal resolutions normally exists. Fine-spatial-resolution images have low temporal resolutions, whereas coarse-spatial-resolution images have high temporal repetition rates. A novel superresolution change detection method (SRCD) is proposed to detect land-cover changes at both fine spatial and temporal resolutions with the use of a coarse-resolution image and a fine-resolution land-cover map acquired at different times. SRCD is an iterative method that involves endmember estimation, spectral unmixing, land-cover fraction change detection, and superresolution land-cover mapping. Both the land-cover change/no-change map and from-to change map at fine spatial resolution can be generated by SRCD. In this paper, SRCD was applied to a synthetic multispectral image, a Moderate-Resolution Imaging Spectroradiometer multispectral image, and a Landsat-8 Operational Land Imager multispectral image. The land-cover from-to change maps are found to have the highest overall accuracy (higher than 85%) in all of the three experiments. Most of the changed land-cover patches, which were larger than the coarse-resolution pixel, were correctly detected.

Description: Nonnegative matrix factorization (NMF) is a powerful class of feature extraction techniques that has been successfully applied in many fields, particularly in signal and image processing. Current NMF techniques have been limited to a single-objective optimization problem, in either its linear or nonlinear kernel-based formulation. In this paper, we propose to revisit the NMF as a multiobjective problem, particularly a biobjective one, where the objective functions defined in both input and feature spaces are taken into account. By taking the advantage of the sum-weighted method from the literature of multiobjective optimization, the proposed biobjective NMF determines a set of nondominated, Pareto optimal, solutions. Moreover, the corresponding Pareto front is approximated and studied. Experimental results on unmixing synthetic and real hyperspectral images confirm the efficiency of the proposed biobjective NMF compared with the state-of-the-art methods.

Description: We propose a lightweight sparsity-based algorithm, namely, the basic thresholding classifier (BTC), for hyperspectral image (HSI) classification. BTC is a pixelwise classifier which uses only the spectral features of a given test pixel. It performs the classification using a predetermined dictionary consisting of labeled training pixels. It then produces the class label and residual vector of the test pixel. Since incorporating spatial and spectral information in HSI classification is quite an effective way of improving classification accuracy, we extend our proposal to a three-step spatial–spectral framework. First, every pixel of a given HSI is classified using BTC. The resulting residual vectors form a cube which could be interpreted as a stack of images representing residual maps. Second, each residual map is filtered using an averaging filter. Finally, the class label of each test pixel is determined based on minimal residual. Numerical results on public data sets show that our proposal outperforms well-known support vector machine-based techniques and sparsity-based greedy approaches like simultaneous orthogonal matching pursuit in terms of both classification accuracy and computational cost.

Description: The intrinsic dimensionality (ID) of multivariate data is a very important concept in spectral unmixing of hyperspectral images. A good estimation of the ID is crucial for a correct retrieval of the number of endmembers (the spectral signatures of macroscopic materials) in the image, for dimensionality reduction or for subspace learning, among others. Recently, some approaches to perform spectral unmixing and superresolution locally have been proposed, which require a local estimation of the number of endmembers to use. However, the role of ID in local regions of hyperspectral images has not been properly addressed. Some important issues when dealing with small regions of hyperspectral data can seriously affect the performance of conventional hyperspectral ID estimators. We show that three factors mainly affect local ID estimation: the number of pixels in the local regions, which has to be high enough for the estimations to be relevant, the number of hyperspectral bands which complicates the estimations if the ambient space has a high dimensionality, and the noise, which can be misinterpreted as a signal when its power is important. Here, we review the hyperspectral ID estimators on the literature for local ID estimation, we show how they behave in a local setting on synthetic and real data sets, and we provide some guidelines to make proper use of these estimators in local approaches.

Description: This paper presents an algorithm for the retrieval of ozone profiles from the Cross-track Infrared Sounder (CrIS) aboard the Suomi National Polar-orbiting Partnership satellite using a nonlinear optimal estimation method. The issue of channel selection is discussed. Based on a sensitivity analysis, we selected a spectral range of 990–1070 cm −1 for the ozone profile retrievals. Compared with ERA-Interim ozone profiles and eigenvector regression method profiles, the ozone climatology profile is better able to construct the a priori state. The retrieved CrIS profiles are in good agreement with smoothed high-vertical-resolution ozonesonde profiles. An analysis of the information content of the CrIS retrievals demonstrates that the CrIS measurements can provide useful information for capturing the spatial and temporal variations in ozone and are insensitive to ozone below 400 hPa. An error analysis revealed that smoothing error represents the main error source for the retrieved CrIS ozone profiles.

Description: This paper investigates the bistatic scattering from randomly corrugated surfaces with irregular grooves. For a given incident wave, the surface fields and the scattered field were computed by the method of moments in which the rooftop basis function was used to account for fast phase changes due to steep surface slopes. The total scattered field is decomposed into coherent and incoherent components to analyze their respective contributions. We found that, for randomly corrugated surfaces with irregular ridges or grooves, the coherent scattering is profound at several scattering angles with strong main lobes, whose beamwidths are strongly correlated with the ridge density. The numerical simulation has shown that the lobe angular shift away from the specular direction is quasi-linearly dependent on the ridge density, suggesting that the coherent scattering pattern substantially contains the surface geometric information. We expect this paper to offer deeper understanding of coherent imaging of rough surface and to help in designing a novel imaging system.

Description: A wind scatterometer measures the normalized radar cross section $sigma^{circ}$ of the Earth's surface in order to estimate the ocean near-surface wind. Each measurement is the weighted integral of $sigma^{circ}$ over an area, so samples of $sigma^{circ}$ are filtered by a measurement spatial response function (SRF). Enhanced-resolution $sigma^{circ}$ data may be produced from $sigma^{circ}$ measurements and their associated SRF using image reconstruction algorithms. These enhanced-resolution data products are useful for land and ice studies in addition to high-resolution ocean wind retrieval. The spatial resolution and noise may be improved by combining data for multiple passes, trading off temporal resolution for spatial resolution. In this paper, we consider the production of enhanced-resolution $sigma^{circ}$ image reconstruction from the Advanced Scatterometer (ASCAT) on the MetOp satellites for land and ice regions. Two previously introduced image reconstruction algorithms, the weighted AVErage (AVE) and Scatterometer Image Reconstruction (SIR), are applied to ASCAT data, as well as a conventional gridding method. We select optimum values of the reconstruction parameters for ASCAT. The imaging algorithms are compared and evaluated, as quantified by the spatial resolution, pixel mean and variance, and pixel correlation of the produced images. The effective spatial resolution of the AVE and SIR reconstructed images is on the order of 15 to 20 km, an improvement over the nominal spatial resolution of 25 and 50 km for th- spatially averaged swath-oriented ASCAT L1B products.

Description: Recent classification-oriented proposals to thematic maps building from hyperspectral images have used both semisupervised approaches and spatial information for correction of spectral classification. Semisupervised approaches enrich the training data set adding similar samples to each class, whereas spatial correction is based on the natural assumption of thematic class spatial compactness. In this paper, we propose and validate the following innovations: 1) a new spectral classifier, which is called anticipative hybrid extreme rotation forest (AHERF); 2) a spatial–spectral semisupervised approach; and 3) a final spatial classification correction step. The novel heterogeneous ensemble learning approach AHERF starts with a model selection phase, using a small subsample of the training data, in order to define a ranking-based selection probability distribution of the classifier architectures that will be used in the ensemble, so that the architecture best adapted to the data domain will be used more frequently to train individual classifiers in the ensemble. After this initial phase, AHERF trains a heterogeneous ensemble applying random rotations to bootstrapped samples of the remaining training data, aiming to obtain diversified and data-domain adapted individual classifiers. The natural assumption that spatially close pixels will most likely have highly correlated values is exploited in two phases of the process pipeline. First, semisupervised label assignment is supported by spectral similarity and spatial proximity. Unsupervised spectral similarity is detected by latent class discovery. In this paper, we use a clustering algorithm (i.e., k-means). Second, maximizing class spatial compactness removes classification errors that appear as speckle noise in the classification image. The whole approach aims to use minimal sets of labeled pixels for training, which we call the seed training data set. Testing results are computed over the entire image ground trut- . For comparison, we provide results in several steps: 1) of classification by AHERF and competing classifiers built by semisupervised training and 2) after spatial correction. We validate the approach on several conventional benchmarking images, achieving results which are comparable with state-of-the-art approaches.

Description: The empirical results of a global navigation satellite systems reflectometry (GNSS-R) experiment onboard the Balloon EXperiments for University Students (BEXUS) 17 stratospheric balloon performed north of Sweden over boreal forests show that the power of the reflected signals is nearly independent of the platform height for a high coherent integration time $T_c = 20 mbox{ms}$ . This experimental evidence shows a strong coherent component in the forward scattered signal, as compared with the incoherent component, that allows to be tracked. The bistatic coherent reflectivity is also evaluated as a function of the elevation angle, showing a decrease of ∼6 dB when the elevation angle increases from 35° to 70°. The received power presents a clearly multimodal behavior, which also suggests that the coherent scattering component may be taking place in different forest elements, i.e., soil, canopy, and through multiple reflections canopy–soil and soil–trunk. This experiment has provided the first GNSS-R data set over boreal forests. The evaluation of these results can be useful for the feasibility study of this technique to perform biomass monitoring that is a key factor to analyze the carbon cycle.

Description: Most previous haze/cloud detection methods for Landsat imagery, e.g., haze optimized transformation (HOT), cannot adequately suppress land surface information and, in particular, often overestimate haze thickness over bright surfaces. This paper proposes an iterative HOT (IHOT) for improving haze detection with the help of a corresponding clear image. With an iterative procedure of regressions among HOT, the reflectance difference at the top of atmosphere (TOA) between hazy and clear images, and TOA reflectances of hazy and clear images, the land surface information can be removed, and the iterative HOT (IHOT) result is derived to spatially characterize the haze contamination in the Landsat images. A group of Landsat images that were acquired in different landscapes and seasons were used to test IHOT. Visual comparisons indicate that IHOT performed better than previous haze detection methods for images that were acquired in diverse landscapes and also performed robustly for hazy images that were acquired at different seasons when using the same reference clear image. Additionally, two indirect quantitative validations were used to illustrate that IHOT can provide the best transformation for accurately determining haze information. Therefore, it is expected that the proposed IHOT method will be used for automatic cloud/haze detection for large numbers of Landsat images if data sets of clear Landsat imagery are available.

Description: In this paper, the formulation of reverse time migration (RTM) is improved for impulse borehole radar imaging in the subsurface scenarios with layered media. By fully adopting the prior information of surrounding media, the time gating function is designed and applied to the incident wave field and scattering wave field for each imaging point, which strengthens the correlation between the wave fields in the time domain. The clutters partly caused by the multiple reflections between different media layers are suppressed due to the gating function. A normalized zero-offset cross correlation with gated samples is conducted and used to weight the result of RTM. The improved approach is compared with the conventional RTM, the back-projection method, and the Stolt migration algorithm with synthetic data and is then validated by a single-borehole radar experiment in a layered media scenario. The results demonstrate that the developed approach is superior to the conventional methods in locating targets and robust in complex subsurface environments.

Description: Many research studies have investigated surface parameter inversion for bare soils. This paper attempts to take into account the agricultural fields with crop residues and fields under low vegetation cover in addition to bare soil fields. An integrated surface parameter inversion scheme (ISPIS) is proposed to invert surface parameters in these agricultural fields based on the analysis of $H{-}alpha$ parameters at the early crop growing stages, in which the calibrated integral equation model (CIEM) is adopted to invert surface parameters for bare soils, and an adaptive two-component decomposition combined with the CIEM and a simplified adaptive volume scattering model is developed for fields with crop residues and under low vegetation cover. Fully polarimetric RADARSAT-2 data with ground truth collected on April 29 and May 9 in 2013 and from May to June in 2014 are used for validation. Compared with other methods, the derived volumetric soil moisture (MV) and surface roughness (KS) of all agricultural fields are consistent with verifiable observations with the lowest overall root mean square error: 6.12 [vol.%] and 0.48, respectively, when all sample sites are considered.

Description: A high-resolution 3-D hydrodynamic model capable of simulating far wakes of ships has been implemented using computational fluid dynamics software. We feed the surface velocity field produced by the hydrodynamic model into a numerical radar imaging model to simulate synthetic aperture radar (SAR) signatures of the wake. Potential capabilities of this modeling method are demonstrated for an example of wind stress effects on the centerline (turbulent) ship wake. The numerical simulations show that an interaction of the wind-induced surface current with circulations in the ship wake results in a convergence zone on the upwind side of the centerline wake and a divergence zone on the downwind side. In the simulated radar image, the convergence zone appears to be bright because of enhanced surface roughness and radar backscattering. The divergence zone looks dark due to an attenuation of short gravity capillary waves and a corresponding reduction of the backscattered power. This combined hydrodynamic and radar imaging model predicts an asymmetry of the centerline wake with respect to the wind direction, which is consistent with observed ship wake signatures in high-resolution satellite SAR images. The approach developed in this work could be also useful for simulations of other natural and artificial fine-scale features on the sea surface (sharp frontal interfaces, freshwater plumes, etc.) and their interpretation in high-resolution SAR imagery.

Description: The majority of multispectral (MS) pansharpening methods may be labeled as spectral or spatial , depending on whether the geometric details that shall be injected into the interpolated MS bands are extracted from the panchromatic (P) image by means of a spectral transformation of MS pixels or a spatial transformation of the P image, achieved by means of linear shift-invariant digital filters. Spectral methods are known as component substitution; spatial methods are based on multiresolution analysis (MRA). In this paper, the authors show that, under the most general conditions, MRA-based pansharpening is characterized by a unique separable low-pass filter, which can be parametrically optimized based on the modulation transfer function (MTF) of the MS instrument, possibly followed by decimation and interpolation stages. This happens for the discrete wavelet transform (DWT) and its undecimated version (UDWT), for the “à-trous” wavelet (ATW) transform and its decimated version, i.e., the generalized Laplacian pyramid (GLP), and for nonseparable wavelet transforms, such as the nonsubsampled contourlet transform (NSCT). Hybrid methods, in which MRA fusion is performed on the intensity component derived from a spectral transformation, are equivalent to MRA fusion with a specific detail injection model. ATW and GLP are preferable to DWT, UDWT, and NSCT, because of computational benefits and of a looser choice of the low-pass filter, unconstrained from the requirement of generating a perfect reconstruction filter bank. Ultimately, GLP outperforms ATW, because its decimation and interpolation stages allow the aliasing impairments intrinsically present in the original MS bands to be removed from the pansharpened product.

Description: Multiyear ice (MYI) characteristics can be retrieved from passive or active microwave remote sensing observations. One of the algorithms that combine both observations to identify partial concentrations of ice types (including MYI) is the Environment Canada Ice Concentration Extractor (ECICE). However, cycles of warm–cold air temperature trigger wet–dry cycles of the snow cover on MYI surface. Under wet snow conditions, anomalous brightness temperature and backscatter, similar to those of first-year ice (FYI), are observed. This leads to misidentification of MYI as being FYI, hence decreasing the estimated MYI concentration suddenly. The purpose of this paper is to introduce a correction scheme to restore the MYI concentration under this condition. The correction is based on air temperature records. It utilizes the fact that the warm spell in autumn lasts for a short period of time (a few days). The correction is applied to MYI concentration retrievals from ECICE using an input of combined QuikSCAT and AMSR-E data, acquired over the Arctic region in a series of autumn seasons from 2003 to 2008. The correction works well by replacing anomalous MYI concentrations with interpolated ones. For September of the six years, it introduces over $0.1times 10^{6} mbox{km}^{2}$ MYI area, except for 2005. Due to the regional effect of warm air spells, the correction could be important in the operational applications where ice concentrations are crucial on small scale and mesoscale.

Description: This paper presents a kernel-based feature selection method for the classification of hyperspectral images. The proposed method aims at selecting a subset of the original features that are both 1) relevant (discriminant) for the considered classification problem, i.e., preserve the functional relationship between input and output variables, and 2) invariant (stable) across different domains, i.e., minimize the data-set shift between the source and the target domains. Domains can be associated with hyperspectral images collected either on different geographical areas or on the same area at different times. We propose a novel measure of data-set shift for evaluating the domain stability, which computes the distance of the conditional distributions between the source and target domains in a reproducing kernel Hilbert space. Such a measure is defined on the basis of the kernel embeddings of the conditional distributions resulting in a nonparametric approach that does not require estimating the distribution of the classes. The adopted search strategy is based on a multiobjective optimization algorithm, which optimizes the two terms of the criterion function for the estimation of the Pareto-optimal solutions. This results in an effective approach of performing feature selection in a transfer learning setting. The experimental results obtained on two hyperspectral images show the effectiveness of the proposed method in selecting features with high generalization capabilities.

Description: Numerous observations and modeling results have shown that there is noticeable directional variation in the solar-induced chlorophyll fluorescence (SIF), and this has not been well investigated. In this paper, 16 multiangular spectral observations were carried out on winter wheat to assess the bidirectional SIF emission. First, the bidirectional SIF emission was retrieved from the spectral measurements made by a high-performance QE Pro spectrometer and an automatic multiangle observation system using the 3FLD algorithm. The bidirectional shape of the SIF emission was found to be similar to that of the canopy reflectance in the solar principal plane, with a mean correlation coefficient of 0.94 and 0.97 at the O 2 -B and O 2 -A bands, respectively. The modified Rahman–Pinty–Verstraete (MRPV) model, a semiempirical bidirectional reflectance distribution function (BRDF) model, was then employed to describe the bidirectional variation in the SIF and reflectance with a mean root-mean-square-error value of 0.036 and 0.041 mW m −2 sr −1 nm −1 for the SIF at the O 2 -B and O 2 -A bands, respectively. Finally, both the bidirectional reflectance and SIF were BRDF corrected to nadir using the MRPV model. Most of the directional variation was successfully corrected by this method—the mean correction ratios were 87% and 81% for the reflectance at the O 2 -B and O 2 -A bands and 84% and 72% for the SIF at the O 2 -B and O 2 -A bands, respectively. Therefore, the SIF emission cannot be regarded as isotropic, and the high similarity between the bidirectional SIF and reflectance, together with the BRDF correction results, indicates that the bidirectional SIF emission can be adjusted using either the BRDF reflectance models or prior knowledge.

Description: Recently, sparse signal representation of image patches has been explored to solve the pansharpening problem. Although these proposed sparse-reconstruction-based methods lead to promising results, three issues remained unsolved: 1) high computational cost; 2) no consideration given to the possibility of mutually correlated information in different multispectral channels; and 3) requirement that the spectral responses of the panchromatic (Pan) image and the multispectral image cover the same wavelength range, which is not necessarily valid for most sensors. In this paper, we propose a sophisticated sparse image fusion algorithm, which is named “jointly sparse fusion of images” (J-SparseFI). It is based on the earlier proposed sparse fusion of images (SparseFI) algorithm and overcomes the aforementioned three drawbacks of the existing sparse image fusion algorithms. The computational problem is handled by reducing the problem size and by proposing a fully parallelizable scheme. Moreover, J-SparseFI exploits the possible signal structure correlations between multispectral channels by introducing the joint sparsity model (JSM) and sharpening the highly correlated adjacent multispectral channels together. This is done by exploiting the distributed compressive sensing theory that restricts the solution of an underdetermined system by considering an ensemble of signals being jointly sparse. J-SparseFI also offers a practical solution to overcome spectral range mismatch between the Pan and multispectral images. By means of sensor spectral response and channel mutual correlation analysis, the multispectral channels are assigned to primary groups of joint channels, secondary groups of joint channels, and individual channels. Primary groups of joint channels, individual channels, and secondary groups of joint channels are then reconstructed sequentially, by the JSM or by modified SparseFI, using a dictionary trained from the Pan image or previously reconstructed- high-resolution multispectral channels. A recipe of how to choose appropriate algorithm parameters, including the most crucial regularization parameter, is provided. The algorithm is evaluated and validated using WorldView-2-like images that are simulated using very high resolution airborne HySpex hyperspectral imagery and further practically demonstrated using real WorldView-2 images. The algorithm's performance is compared with other state-of-the-art methods. Visual and quantitative analyses demonstrate the high quality of the proposed method. In particular, the analysis of the difference images suggests that J-SparseFI is superior in image resolution recovery.

Description: This paper considers some of the issues of radiometer brightness image formation and reconstruction for use in the NASA-sponsored Calibrated Passive Microwave Daily Equal-Area Scalable Earth Grid 2.0 Brightness Temperature Earth System Data Record project, which generates a multisensor multidecadal time series of high-resolution radiometer products designed to support climate studies. Two primary reconstruction algorithms are considered: the Backus–Gilbert approach and the radiometer form of the scatterometer image reconstruction (SIR) algorithm. These are compared with the conventional drop-in-the-bucket (DIB) gridded image formation approach. Tradeoff study results for the various algorithm options are presented to select optimum values for the grid resolution, the number of SIR iterations, and the BG gamma parameter. We find that although both approaches are effective in improving the spatial resolution of the surface brightness temperature estimates compared to DIB, SIR requires significantly less computation. The sensitivity of the reconstruction to the accuracy of the measurement spatial response function (MRF) is explored. The partial reconstruction of the methods can tolerate errors in the description of the sensor measurement response function, which simplifies the processing of historic sensor data for which the MRF is not known as well as modern sensors. Simulation tradeoff results are confirmed using actual data.

Description: Until now, most polarimetric synthetic aperture radar interferometry (PolInSAR) research has been based on full-polarization (HH $+$ HV $+$ VH $+$ VV) SAR data, which can provide complete polarimetric information but usually have a smaller swath and lower spatial resolution than dual-polarization (dual-pol) data. Some existing researches concern the dual-pol PolInSAR; however, these works did not perform the coherence optimization process which may make the inversion unstable. In this paper, the PolInSAR technique based on dual-pol (HH $+$ HV) SAR data is investigated in order to demonstrate its validity for forest height retrieval and thus show that PolInSAR is better able to meet the requirements of global-scale research. We extend the three-stage inversion process and coherence optimization algorithm to dual-pol PolInSAR. In addition, based on the random volume over ground model, a search method for finding the volume-only coherence on ambiguous line segments is proposed. Finally, the dual-pol PolInSAR technique is applied to forest height estimation using airborne L-band SAR data acquired by the E-SAR system over the Traunstein test site in Germany. The forest heights estimated by dual-pol PolInSAR are compared with those estimated using the full-pol mode and also with measurements made in situ . The results show that dual-pol PolInSAR can obtain similar estimated forest heights to the full-pol mode and also that the search method for volume-only coherence retrieval can improve the inversion accuracy. The coefficient of determination $(r^{2})$ for the relation between the dual-pol PolInSAR an- the in situ measurements is 0.7287.

Description: Spectral mixture analysis (SMA) is widely used to quantify the fraction of each component (endmember) of mixed pixels that contain spectral signals from more than one land surface type. Generally, nonlinear SMA (NSMA) outperforms linear SMA (LSMA) in the vegetation (tree, shrub, crop, and grass) and soil mixture case because NSMA considers the significant multiple scattering that exists for these mixtures. However, compared to LSMA, the bilinear NSMA method, which is a typical physical-based NSMA method, is undermined by its susceptibility to the collinearity effect. In this paper, a two-step constrained NSMA method (referred to as TsC-NSMA) is proposed to mitigate the collinearity effect in the bilinear NSMA method. The theoretical maximum likelihood range is mathematically derived for each endmember fraction, and the ranges are used as additional constraints for the bilinear NSMA method to optimize the unmixing results. Three different data sets, including simulated spectral data, an in situ ground plot spectral measurement, and a Landsat8 Operational Land Imager image, were used to assess the performance of the TsC-NSMA method. The results indicated that TsC-NSMA achieved the highest estimation accuracy for all mixed scenarios which either contain severe endmember collinearity or high noise levels, thereby suggesting its ability to mitigate the collinearity effect in the bilinear NSMA method with the potential to improve the estimation of endmember fractions in practical applications.

Description: The first Visible Infrared Imaging Radiometer Suite (VIIRS) is onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. As a primary sensor, it collects imagery and radiometric measurements of the land, atmosphere, cryosphere, and oceans in the spectral regions from visible (VIS) to long-wave infrared. NASA's National Polar-orbiting Partnership (NPP) VIIRS Characterization Support Team has been actively involved in the VIIRS radiometric and geometric calibration to support its Science Team Principal Investigators for their independent quality assessment of VIIRS Environmental Data Records. This paper presents the performance assessment of the radiometric calibration stability of the VIIRS VIS and NIR spectral bands using measurements from SNPP VIIRS and Aqua MODIS simultaneous nadir overpasses and over the invariant surface targets at the Libya-4 desert and Antarctic Dome Concordia snow sites. The VIIRS sensor data records (SDRs) used in this paper are reprocessed by the NASA SNPP Land Product Evaluation and Analysis Tool Element. This paper shows that the reprocessed VIIRS SDRs have been consistently calibrated from the beginning of the mission, and the calibration stability is similar to or better than MODIS. Results from different approaches indicate that the calibrations of the VIIRS VIS and NIR spectral bands are maintained to be stable to within 1% over the first three-year mission. The absolute calibration differences between VIIRS and MODIS are within 2%, with an exception for the 0.865- $mumbox{m}$ band, after correction of their spectral response differences.

Description: This paper addresses the problem of imaging targets using radio signals transmitted by the nodes of a wireless sensor network. The sensors are assumed to be simple wireless communication devices, e.g., Wi-Fi cards, which are capable to measure only the received signal power. A fully incoherent linear inverse scattering approach is herein tackled, and a Rytov-based model is considered. The main contribution of this paper is concerned with the evaluation of the imaging performance achievable when phase information is not exploited in the inversion stage. The analysis is worked out with the singular value decomposition tool, in order to foresee the resolution limits for a given network arrangement and noise level on data. The popular coherent inverse scattering model of Born approximation is also considered as benchmark for comparison purposes. Numerical results based on full-wave data are reported to highlight the actual capabilities of the incoherent tomography.

Description: This paper addresses the mitigation of wind turbine clutter (WTC) in weather radar data in order to increase the performance of existing weather radar systems and to improve weather analyses and forecasts. We propose a novel approach for this problem based on signal separation algorithms. We model the weather signal as group sparse in the time–frequency domain; in parallel, we model the WTC signal as having a sparse time derivative. In order to separate WTC and the desired weather returns, we formulate the signal separation problem as an optimization problem. The objective function to be minimized combines total variation regularization and time–frequency group sparsity. We also propose a three-window short-time Fourier transform for the time–frequency representation of the weather signal. To show the effectiveness of the proposed algorithm on weather radar systems, the method is applied to simulated and real data from the next-generation weather radar network. Significant improvements are observed in reflectivity, spectral width, and angular velocity estimates.

Description: A global sensitivity analysis utilizing the extended Fourier amplitude sensitivity test is used to determine the parameter sensitivity of the L-band microwave emission of the biosphere (L-MEB) model. The results are analyzed from two perspectives of calibration and inversion. First, the parameters of surface soil moisture, soil roughness factor, vegetation optical depth at nadir, and effective land surface temperature are the four most sensitive parameters in the L-MEB model, demonstrating their possibility to be retrieved in the multiparameter retrieval approaches. Then, the high total sensitivity index (TSI) values of surface soil temperature in the analyses emphasize the importance of high-precision land surface temperature data in the surface soil moisture retrievals, especially for rougher or more vegetated surface conditions. Finally, our analysis indicates that TSI values are high for the soil surface roughness and vegetation optical depth model parameters but low for the vegetation structure, single scattering albedo, and soil roughness coefficient model parameters at incidence angles near nadir. This suggests that calibration experiments performed at small incidence angles may be appropriate for some but not all of the model parameters, which characterize the effect of soil surface roughness and vegetation on the terrestrial brightness temperature. Consequently, new calibration procedures that account for the different relative sensitivities of these model parameters at larger incidence angles may need to be developed in the future.

Description: In this paper, dynamical-model-selection-based multimodel ensemble (DMS-MME) technique is developed for skill improvement of monsoon rain prediction in the medium range (i.e., 24–120 h ahead). The data set consists of 24–120 h daily precipitation forecasts from five state-of-the-art global circulation models (GCMs), i.e., European Centre for Medium Range Weather Forecasts (Europe), National Center for Environmental Prediction (USA), China Meteorological Administration (China), Canadian Meteorological Centre (Canada) and U.K. Meteorological Office (U.K.). The DMS-MME forecasts are constructed during the monsoon months (JJAS) for the years 2008–13 over the Indian mainland. For the training and verification purposes, India Meteorological Department rainfall is used. The forecast skill of the DMS-MME model has been compared with the performance of individual models and regression-based MME model. Further, to remove the nonnormality of rainfall distribution, square-root and logarithmic transfer functions are used for normalizing the precipitation data. The impact of these transfer functions on the forecast skill of the DMS-MME model has been assessed. The forecast skill of the proposed model is evaluated using the standard statistical measures. DMS-MME forecasts carries higher skill in terms of verification scores compared with the MME forecasts up to 120 h. It has been found that using the DMS-MME approach with a square-root transfer function (SDMS-MME) gives the best results. SDMS-MME outperforms the operational models and the regression-based MME at all forecast steps.

Description: In this paper, we propose an improved attributed scattering model to mathematically unify the scattering models of several canonical primitives. These primitives include not only point- and line-segment-scatterers, such as trihedral, cylinder, dihedral, and rectangular plane, but also arc scatterers, such as sphere and top-hat. The estimation of the model parameters can be posed as an ill-posed linear inverse problem. To overcome the ill-posedness, we employ the incremental sparse Bayesian learning method to realize the sparsity-driven continuous parameter estimation. Inverse scattering experiments demonstrate that the proposed methodology not only provides desirable sparse representations of the target scattering response but is also able to capture richer geometrical information than the existing models.

Description: In this paper, the retrieval of sea surface current velocity from vertically polarized (V-pol) X-band marine radar data is presented. Three different methods, including the iterative least square approach, the normalized scalar product method, and the polar current shell algorithm, that have been used for horizontally polarized data are employed here. A comprehensive comparison of the performance of the three methods is conducted using the radar images collected by the V-pol radar deployed on the Forschungsplattformen in Nord- und Ostsee No. 3 (FINO3) offshore research platform and the acoustic Doppler current profiler (ADCP) data in the North Sea. The results indicate that all three methods are capable of providing reliable current speed and direction measurements from V-pol data, with similar performance. Based on the experimental data for which the current magnitude is less than 0.5 m/s, the radar-derived results agree best with the ADCP data at a depth of 6–8 m, with the root mean square difference for current velocity $x$ - and $y$ -components being 7.2–8.9 cm/s. The correlation coefficients between the radar-derived and ADCP-measured current velocity components are as high as 0.87–0.93.

Description: Cloud covers, which generally appear in optical remote sensing images, limit the use of collected images in many applications. It is known that removing these cloud effects is a necessary preprocessing step in remote sensing image analysis. In general, auxiliary images need to be used as the reference images to determine the true ground cover underneath cloud-contaminated areas. In this paper, a new cloud removal approach, which is called multitemporal dictionary learning (MDL), is proposed. Dictionaries of the cloudy areas (target data) and the cloud-free areas (reference data) are learned separately in the spectral domain. The removal process is conducted by combining coefficients from the reference image and the dictionary learned from the target image. This method could well recover the data contaminated by thin and thick clouds or cloud shadows. Our experimental results show that the MDL method is effective in removing clouds from both quantitative and qualitative viewpoints.

Description: Launched in September 2014, RapidScat is currently operating on the International Space Station (ISS). RapidScat estimates ocean vector winds via the measurement of the normalized radar coefficient $(sigma^0)$ of the ocean's surface. Measurements are also collected over land. The ISS orbit permits, for the first time, the observation of the diurnal variation in Ku-band $sigma^0$ at mid- to high-incidence angles. To complement calibration efforts over the ocean, in this paper the calibration and validation of the $sigma^0$ measurements are performed using natural land targets, namely the Amazon and Congo rainforests. The diurnal $sigma^0$ cycle of the targets is estimated with respect to incidence angle, azimuth angle, and season using measurements from previous sensors. Understanding this diurnal backscatter response enables the comparison of RapidScat measurements with measurements from the QuikSCAT, NASA Scatterometer, SeaWinds, and Oceansat-II scatterometers. RapidScat $sigma^0$ measurements are found to be consistent but biased low compared to those of QuikSCAT by up to 0.3 dB. The effectiveness of slice balancing is evaluated and found to be dependent on the pitch of the ISS. Extreme pitches of the ISS are also found to introduce azimuth dependencies in egg measurements. By accounting for seasonal and diurnal cycles, we find that the rainforests are well suited for scatterometer sensor cross-calibration, even for disjoint years.

Description: Remote sensing images are often contaminated by varying degrees of stripes, which severely affects the visual quality and subsequent application of the data. Unlike with conventional methods, we achieve the destriping by separating the stripe component based on a full analysis of the various stripe properties. Under an optimization framework, an $ell^{0}$ -norm-based regularization is used to characterize the global sparse distribution of the stripes. In addition, difference-based constraints are adopted to describe the local smoothness and discontinuity in the along-stripe and across-stripe directions, respectively. The alternating direction method of multipliers is applied to solve and accelerate the model optimization. Experiments with both simulated and real data demonstrate the effectiveness of the proposed model, in terms of both qualitative and quantitative perspectives.

Description: The polarization diversity of the phase causes ambiguities in the estimation of displacements using differential interferometry. Over natural surfaces such as vegetated areas, the magnitude of these ambiguities is potentially related to complex dynamic processes such as vegetation growth. As the properties and possible origins of such diversity (besides noiselike influences) over changing vegetation canopies are virtually unknown, we propose to investigate them empirically using an L-band zero-baseline data set covering one growing season over different agricultural crops. We frequently observe HH–VV phase differences exceeding $0.5pi$ , corresponding to a displacement discrepancy of 3 cm. The HH–VV phase difference and other properties of the polarimetric coherence regions (e.g., the shape and the relation to the in situ observed biomass) vary with the crop type. The observations over wheat and barley and, to a lesser extent, rape suggest the presence of birefringence within the canopy. By contrast, those over maize and sugar beet, while also showing phase diversity, cannot be explained by birefringence or similarly simple models. Irrespective of the origin of this dependence, its presence and systematic nature indicate the potential importance of vegetation effects in differential interferometry, which may limit the accuracy of the estimated deformations over vegetated areas.

Description: Speckle is a granular disturbance that affects synthetic aperture radar (SAR) images. Over the last three decades, many methods have been proposed for speckle reduction, where a tradeoff between despeckling and detail preservation is required. As an attempt to balance the performance on both sides, in this paper, we propose a 2-D S transform shrinkage algorithm using adaptive soft threshold for SAR image despeckling. It follows the idea of the wavelet shrinkage algorithm, but extends its major steps to take into account the peculiarities of S transform, i.e., adding adaptivity in the estimation of speckle standard deviation and threshold function, in an optimized computation procedure. Homogeneous and heterogeneous SAR images are used for quantitative evaluations, and both vintage and prevailing algorithms are used for comparison, which demonstrates the validity of the proposed method. Additionally, some instructive pieces of advice are given on the selection of suitable parameters of the proposed method under different circumstances.

Description: High-resolution thermal infrared (TIR) remote sensing has a wide range of applications. In this paper, we describe the different applications and requirements identified in a literature review and during a consultation meeting with researcher experts in different fields. As a result, more than 30 applications were identified within three different fields: 1) land and solid Earth; 2) health and hazards; and 3) security and surveillance. A complete set of requirements (spatial, temporal, and radiometric resolution, algorithms used, and supporting data, among others) for each application is also provided. The results presented in this paper provide useful information to enhance the importance of high-resolution TIR data for civil applications and may serve as a reference document for future TIR mission concepts.

Description: During the growing season, the photosynthesis and growth of boreal forests are regulated by physiological responses to environmental factors. Physiological variations affect the spectral properties of leaves. Linking canopy-level spectral reflectance to leaf-level processes for monitoring forest seasonal physiology using satellite images is hindered by view and illumination effects and variations in canopy structure. To better understand the connection between the two structural levels, we used nine narrow-band vegetation indices (VIs) derived from Hyperion imagery to track the seasonal dynamics of boreal forest stands: the photochemical reflectance indices (PRI and $text{PRI}_{515}$ ) related to the xanthophyll cycle, the red edge (RE) index, the Maccioni (Macc) and the green normalized difference vegetation index related to chlorophyll concentration $({C}a+b)$ , the carotenoid simple ratio and Gitelson carotenoid concentration index related to carotenoid concentration $({C}x+c)$ , the normalized difference vegetation index related to fractional cover, and the plant senescence reflectance index related to the ${C}x+c/{C}a+b$ ratio. As ground truth, we used measurements of exposed pine shoot light use efficiency (LUE) and photosynthesis. Over the study period (May to August), LUE and photosynthesis were best correlated with the chlorophyll VIs Macc and RE. Both indices also exhibited the lowest coefficient of variation in association with forest structure. PRI, on the other hand, was affected by canopy structure and observation geometry and was uncoupled from LUE during the growing season. Our findings demonstrate that the photosynthesis and productivity of boreal forests in the growing s- ason are best tracked using VIs related to total pigment concentration (i.e., chlorophyll).