ALBERT

Description: Postdisaster search and rescue is an important application of ultrawideband (UWB) radar systems, which mainly detect trapped victims by their respiratory-motion response. The development of a respiratory-motion detection (RMD) algorithm that can eliminate nonstationary clutter and noise is a challenging task for the application. A new algorithm is proposed to deal with the task in this letter. It uses the multichannel singular spectrum analysis (MSSA) technique to reconstruct the respiratory-motion response detected by a UWB radar. During the reconstruction, the periodicity and range interrelation characteristics of the response are exploited to adaptively identify signal subspaces. The performance of the algorithm is verified both by simulated and real data. The results show its improved performance over the reference algorithms, e.g., a singular-value-decomposition-based algorithm. The adaptive-MSSA-based RMD algorithm has great promise not only in practical use but also for future research of UWB-radar-based human being remote sensing.

Description: Land–sea contamination observed in Soil Moisture and Ocean Salinity (SMOS) brightness temperature images is found to have two main contributions: the floor error inherent of image reconstruction and a multiplicative error either in the antenna temperature or in the visibility samples measured by the correlator. The origin of this last one is traced down to SMOS calibration parameters to yield a simple correction scheme, which is validated against several geophysical scenarios. Autoconsistency rules in interferometric synthesis together with redundant and complementary calibration procedures provide a robust SMOS calibration scheme.

Description: This letter proposes a novel algorithm, which is based on the generalized method of moments (GMM), for the estimation and correction of phase errors induced in synthetic aperture radar (SAR) imagery. The GMM algorithm is used to replace the original phase-estimation kernel in the basic structure of the phase-gradient-autofocus algorithm. Since this novel algorithm does not require the observed signal to be a certain distribution model, it is able to estimate arbitrary phase errors. The GMM algorithm has the ability of estimating range-dependent phase errors, which makes it an efficient estimator. As a result, higher accuracy of the estimated phase errors and a better focused image can be achieved. Excellent results have been obtained in autofocusing and imaging experiments on real SAR data.

Description: Strong clutter reflections of terrain and marine surfaces obscure the contrast between the target-of-interest and clutter (terrain and marine surface reflections) in synthetic aperture radar (SAR) images and consequently hinder the efficiency of image interpretation and analysis. To overcome this problem, this letter proposes an efficient clutter suppression method in SAR images, which is named shedding irrelevant patterns (SIP). The essence is to construct a regression function that can suppress clutter and preserve the target patterns concurrently. We assume that the clutter is irrelevant to the target-of-interest and distinguishable in patterns in terms of image-pixel distribution and intensity (spatial information). Experimental results show the efficiency of the proposed method in both clutter suppression and target pattern preservation.

Description: A concern in hyperspectral image classification is the high number of required training samples. When traditional classifiers are applied, feature reduction (FR) techniques are the most common approaches to deal with this problem. Subspace-based classifiers, which are developed based on high-dimensional space characteristics, are another way to handle the high dimension of hyperspectral images. In this letter, a novel subspace-based classification approach is proposed and compared with basic and improved subspace-based classifiers. The proposed classifier is also compared with traditional classifiers that are accompanied by an FR technique and the well-known support vector machine classifier. Experimental results prove the efficiency of the proposed method, especially when a limited number of training samples are available. Furthermore, the proposed method has a very high level of automation and simplicity, as it has no parameters to be set.

Description: To improve the spatial density of measurement points of persistent-scatterer interferometry, distributed scatterer (DS) should be considered and processed. An important procedure in DS interferometry is the phase triangulation (PT). This letter introduces two modified PT algorithms (i.e., equal-weighted PT and coherence-weighted PT) and analyzes the mathematical relations between different published PT methods (i.e., the maximum-likelihood phase estimator, least squares estimator, and eigendecomposition-based phase estimators). The analysis shows that the above five PT methods share very similar mathematical forms with different weight values in the estimation procedure.

Description: Sparse representation-based classifier and its variants have been widely adopted for hyperspectral image (HSI) classification recently. However, sparse representation is unstable so that similar features might obtain significantly different sparse codes. Despite the instability, we find that the sparse codes follow a class-dependent distribution under the structured dictionary consisting of training samples from all classes. Based on this observation, a novel discriminative feature, sparse code histogram (SCH), is developed for HSI classification. By counting the SCH of each sample from the sparse codes of its spatial neighbors, we can statistically obtain the distribution pattern of sparse codes of the class to which the sample belongs, and then treat the SCH as a new feature for classification. To reduce the possible outliers among the neighbors, a shape-adaptive neighborhood extractor is also employed to enhance the stability of the histogram feature. Experimental results demonstrate that SCH enjoys a strong discriminative power, which can achieve notably better performance than several state-of-the-art methods for HSI classification with limited training samples.

Description: Several detection statistics have been proposed for detecting fine ground disturbances between two synthetic aperture radar (SAR) images, such as vehicle tracks. The standard method involves estimating a local correlation coefficient between images. Other methods have been proposed using various statistical hypothesis tests. One of these alternative methods is a generalized likelihood ratio test (GLRT), which compares a full-correlation image model to a no-correlation image model. In this letter, we expand the GLRT to polarimetric SAR data and derive the appropriate GLRT detection statistics. Additionally, we explore relaxing the equal variance/equal polarimetric covariance assumptions used in previous results and find improved performance on macroscopic scene changes.

Description: For downward-looking linear array 3-D synthetic aperture radar, the resolution in cross-track direction is much lower than the ones in range and azimuth. Hence, superresolution reconstruction algorithms are desired. Since the cross-track signal to be reconstructed is sparse in the object domain, compressive sensing algorithm has been used. However, the imaging processing on the 3-D scene brings large computational loads, which renders challenges in both data acquisition and processing. To cover this shortage, truncated singular value decomposition is utilized to reconstruct a reduced-redundancy spatial measurement matrix. The proposed algorithm provides advantages in terms of computational time while maintaining the quality of the scene reconstructions. Moreover, our results on uniform linear array are generally applicable to sparse nonuniform linear array. Superresolution properties and reconstruction accuracies are demonstrated using simulations under the noise and clutter scenarios.

Description: This letter proposes a signal processing method of passive bistatic radar (PBR) exploiting an uncooperative radar as an illuminator. Compared with other opportunity illuminators, the transmitting signal of a radar usually has a better ambiguity function, which leads to a higher range resolution. Two channels are needed in PBR system. The reference channel is used to estimate radar signal parameters and reconstruct directly propagated signal. The surveillance channel is used to receive scattered wave. An array antenna and a simultaneous multibeam algorithm are necessary in the surveillance channel due to the flexible beam scanning of the uncooperative radar. The procedure of the proposed method is explained in detail, which is then followed by a field experiment. Preliminary results from the field experiment show that the proposed method can be applied to target angle and bistatic range measurement successfully.

Description: In order to achieve 3-D imaging with an airborne down-looking linear-array synthetic aperture radar (LASAR), a uniform virtual antenna array may be obtained by aperture synthesis of the cross-track sparse multiple-input–multiple-output array. However, the actual 3-D imaging quality is unavoidably degraded by errors in the virtual element position. In this letter, we investigate the effects of these errors on the forms and the degrees of image quality degradation by decomposing the error-related stochastic processes via an orthogonal transform based on discrete Legendre polynomials. It should be noted that these analyses are helpful for designing a LASAR system and providing a reference for specifying the requisite precision of measurement devices and calibration methods. Finally, we briefly consider the use of calibration methods to eliminate the effects of errors.

Description: In problems where labeled data are scarce, semisupervised learning (SSL) techniques are an attractive framework that can exploit both labeled and unlabeled data. These approaches typically rely on a smoothness assumption such that examples that are similar in input space should also be similar in label space. In many domains, such as remotely sensed hyperspectral image (HSI) classification, the data violate this assumption. In response, we propose a general method by which a neighborhood graph used in SSL is learned using binary classifiers that are trained to predict whether a pair of pixels shares the same label. Working within the framework of semisupervised neural networks (SSNNs), we show that our approach improves on the performance of the SSNN on two HSI data sets.

Description: In this letter, new models for the spatial correlation of sea clutter texture and intensity are proposed as improved versions of current power law models or exponential decay model. The models for texture have three unknown parameters, and thus can be called triparametric models. The structure of the models is a weighted sum of two components, which can describe the decaying process of the correlation coefficient with spatial lags, as well as the periodic behavior due to the existence of transient coherent structures in sea clutter. Unknown parameters are optimized by the nonlinear least square fit method. Models for sea clutter intensity can be obtained through a linear transform for uncorrelated speckle based on the compound-Gaussian representation of sea clutter. The proposed models are validated and compared with current models using S- and C-band measured sea clutter data. Analysis results indicate the effectiveness of the proposed models in that they can describe the behavior of spatial correlation coefficients with higher accuracy.

Description: Terrestrial laser scanning (TLS) has become a popular tool for acquiring source data points which can be used to construct digital elevation models (DEMs) for a wide number of applications. A TLS point cloud often has a very fine spatial resolution, which can represent well the spatial variation of a terrain surface. However, the uncertainty in DEMs created from this relatively new type of source data is not well understood, which forms the focus of this letter. TLS survey data representing four terrain surfaces of different characteristics were used to explore the effects of surface complexity and typical TLS data density (in terms of data point spacing) on DEM accuracy. The spatial variation in TLS data can be decomposed into parts corresponding to the signal of spatial variation (of terrain surfaces) and noise due to measurement error. We found a linear relation between the DEM error and the typical TLS data spacings considered (30–100 mm) which arises as a function of the interpolation error, and a constant contribution from the propagated data noise. This letter quantifies these components for each of the four surfaces considered and shows that, for the interpolation method considered here, higher density sampling would not be beneficial.

Description: A novel way to estimate the live fuel moisture content (LFMC) was explored from the ratio of canopy water content (CWC) and foliage dry biomass (FDB). The CWC was estimated using the PROSAIL (PROSPECT + SAIL) radiative transfer model from the Landsat 8 product. A weak constraint 4-D variational data assimilation method was employed to assimilate the temporally estimated leaf area index into a soil-water-atmosphere-plant (SWAP) model for optimizing the model control variables. Then, the SWAP model was reinitialized with this optimum set of control variables, and better prediction of FDB was obtained. Results showed that a high accuracy level was achieved for the estimated CWC ( $R^{2}=0.91$ , $mbox{RMSE}=84.74 mbox{g/m}^2$ ) and FDB ( $R^2=0.88$ , $mbox{RMSE}=48.54 mbox{g/m} ^2$ ) when compared with in situ measured values. However, the accuracy level of estimated LFMC was poor ( $R^2=0.59$ , $mbox{RMSE} =30.85%$ ) . Further analyses find that the estimated LFMC is reliable for low LFMC but challenged for high LFMC, which indicates that the presented method still makes sense to the assessment of wildfire risk since the wildfire generally occurs when the vegetation is in low LFMC condition.

Description: In this letter, we present the use of experimental human micro-Doppler signature data gathered by a multistatic radar system to discriminate between unarmed and potentially armed personnel walking along different trajectories. Different ways of extracting suitable features from the spectrograms of the micro-Doppler signatures are discussed, particularly empirical features such as Doppler bandwidth, periodicity, and others, and features extracted from singular value decomposition (SVD) vectors. High classification accuracy of armed versus unarmed personnel (between 90% and 97% depending on the walking trajectory of the people) can be achieved with a single SVD-based feature, in comparison with using four empirical features. The impact on classification performance of different aspect angles and the benefit of combining multistatic information is also evaluated in this letter.

Description: Automatic urban area detection in remote sensing images is an important application in the field of earth observation. Most of the existing methods employ feature classifiers and thereby contain a data training process. Moreover, some methods cannot detect urban areas in complex scenes accurately. This letter proposes an automatic urban area detection method that uses multiple features that have different resolutions. First, a downsampled low-resolution image is used to segment the candidate area. After the corner points of the urban area are extracted, a weighted Gaussian voting matrix technique is employed to integrate the corner points into the candidate area. Then, the edge features and homogeneous region are extracted by using the original high-resolution image. Using these results as the input, the processes of guided filtering and contrast enhancement can finally detect accurately the urban areas. This method combines multiple features, such as corner, edge, and regional characteristics, to detect the urban areas. The experimental results show that the proposed method has better detection accuracy for urban areas than the existing algorithms.

Description: In marine sciences, time series are often nonlinear and nonstationary. Adequate and specific methods are needed to analyze such series. In this letter, an application of the empirical mode decomposition method (EMD) associated to the Hilbert spectral analysis (HSA) is presented. Furthermore, EMD-based time-dependent intrinsic correlation (TDIC) analysis is applied to consider the correlation between two nonstationary time series. Four temperature time series obtained from automatic measurements in nearshore waters of the Réunion island are considered, recorded every 10 min from July 2011 to January 2012. The application of the EMD on these series and the estimation of their power spectra using the HSA are illustrated. The authors identify low-frequency tidal waves and display the pattern of correlations at different scales and different locations. By TDIC analysis, it was concluded that the high-frequency modes have small correlation, whereas the trends are perfectly correlated.

Description: Detecting vehicles in aerial images provides important information for traffic management and urban planning. Detecting the cars in the images is challenging due to the relatively small size of the target objects and the complex background in man-made areas. It is particularly challenging if the goal is near-real-time detection, i.e., within few seconds, on large images without any additional information, e.g., road database and accurate target size. We present a method that can detect the vehicles on a 21-MPixel original frame image without accurate scale information within seconds on a laptop single threaded. In addition to the bounding box of the vehicles, we extract also orientation and type (car/truck) information. First, we apply a fast binary detector using integral channel features in a soft-cascade structure. In the next step, we apply a multiclass classifier on the output of the binary detector, which gives the orientation and type of the vehicles. We evaluate our method on a challenging data set of original aerial images over Munich and a data set captured from an unmanned aerial vehicle (UAV).

Description: We compare five slope correction methods developed by Walter et al. , Montes et al. , Schleppi et al. , España et al. , and Gonsamo et al. (referred to as WAL, MON, SCH, ESP, and GON, respectively) using artificial fisheye pictures simulated by graphics software and a lookup table (LUT) retrieval method. The LUT is built by simulating the directional gap fraction as a function of leaf area index (LAI) and average leaf inclination angle (ALIA) using the Poisson law. LAI and ALIA estimates correspond to the case of the LUT that provides the lowest root-mean-square error between the observed gap fractions after slope correction and the simulated ones. Three LAI values (1.5, 3.5, and 5.5), four ALIA values (26.8°, 45°, 57.5°, and 63.2°), and three slope angles (0°, 20°, and 50°) constituted 36 samples of random scenes. ESP is recommended because its results are accurate and independent on the leaf angle distribution (LAD), while GON only performs well for spherical LAD. The three other methods present less good performances with underestimation or overestimation of LAI and/or ALIA depending on the LAD, and the recommended order for them is MON, SCH, and WAL.

Description: In this letter, an improved phase correlation (PC) method based on 2-D plane fitting and the maximum kernel density estimator (MKDE) is proposed, which combines the idea of Stone's method and robust estimator MKDE. The proposed PC method first utilizes a vector filter to minimize the noise errors of the phase angle matrix and then unwraps the filtered phase angle matrix by the use of the minimum cost network flow unwrapping algorithm. Afterward, the unwrapped phase angle matrix is robustly fitted via MKDE, and the slope coefficients of the 2-D plane indicate the subpixel shifts between images. The experiments revealed that the improved method can effectively avoid the impact of outliers on the phase angle matrix during the plane fitting and is robust to aliasing and noise. The matching accuracy can reach 1/50th of a pixel using simulated data. The real image sequence tracking experiment was also undertaken to demonstrate the effectiveness of the proposed PC method with a registration accuracy of root-mean-square error better than 0.1 pixels.

Description: Accurately mapping forest carbon density by combining sample plots and remotely sensed images has become popular because this method provides spatially explicit estimates. However, mixed pixels often impede the improvement of the estimation. In this letter, regression modeling and spectral unmixing analysis were integrated to improve the estimation of forest carbon density for the You County of Hunan, China, using Landsat Thematic Mapper images. Linear spectral unmixing with and without a constraint (LSUWC and LSUWOC) and nonlinear spectral unmixing (NSU) were compared to derive the fractions of five endmembers, particularly forests. Stepwise regression, logistic regression, and polynomial regression (PR) with and without the forest fraction used as an independent variable and the product of the forest fraction image and the map from the best model without the forest fraction were compared. The models were developed using 56 sample plots, and their results were validated using 26 test plots. The decomposition of mixed pixels was assessed using higher spatial resolution SPOT images and a corresponding land cover map. The results showed that 1) LSUWC more accurately estimated the endmember fractions than LSUWOC and NSU, 2) PR had the greatest estimation accuracy of forest carbon, and 3) combining regression modeling and spectral unmixing increased the estimation accuracy by 31%–39%, and introducing the forest fraction into the regressions performed better than the product of forest fraction image and the results from PR without the fraction. This implied that the integrations provided great potential in reducing the impacts of mixed pixels in mapping forest carbon.

Description: This letter proposes a multiresolution technique to address the high computational cost in remote sensing image registration. The scale-invariant feature transform is applied to detect keypoints and descriptors, and then, global information combined with descriptors is utilized to establish keypoint mappings. Keypoints are first classified according to their octaves. Then, in the lowest resolution, the keypoints of the largest octave are mapped with descriptors and the global information, giving an initial affine transformation $T_0$ . In the next octave, the keypoints of the second largest octave are mapped by employing $T_0$ to narrow the space of matching keypoints. By this means, the process of establishing keypoint correspondences is conducted from one resolution (octave) to the next as the obtained transformation gets finer until we get to the highest resolution. Due to the high computational expense of computing global information, the proposed technique is important for aligning large-size remote sensing imagery. Experimental results show that the proposed method can achieve a comparable registration accuracy but with a less computational cost than directly building keypoint mappings on images of large size.

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

Description: In this letter, we present an efficient parallel implementation of composite kernels in support vector machines (SVMs) for hyperspectral image (HSI) classification. Our implementation makes effective use of commodity graphics processing units (GPUs). Specifically, we port the calculation of composite kernels to GPUs, perform intensive computations based on NVidia's compute unified device architecture, and execute the rest of the operations related with control and small data calculations in the CPU. Our experimental results, conducted using real hyperspectral data sets and NVidia GPU platforms, indicate significant improvements in terms of computational effectiveness, achieving near-real-time performance of spatial–spectral HSI classification for the first time in the literature.

Description: Compared with airborne laser scanning, terrestrial laser scanning (TLS) offers ground-based point cloud data of trees and provides greater potential to accurately estimate tree and stand parameters. However, there is a lack of effective methods to accurately identify locations of individual trees from TLS point cloud data. It is also unknown whether the estimation accuracy of the parameters, including tree height (H), diameter at breast height (DBH), and so on, using TLS can meet the requirement of forest management and planning. In this letter, a novel method to effectively process point cloud data and further determine the locations of individual trees in a stand based on the central coordinates of point cloud data on a defined grid according to the largest DBH was developed. Moreover, a point-cloud-data-based convex hull algorithm and the cylinder method were, respectively, used to estimate DBH and H of individual trees. This study was conducted in a pure Chinese fir plantation of 45 trees located in Huang-Feng-Qiao forest farm, You County of Hunan, China. The comparison of the estimated and observed values showed that the obtained tree locations had errors of less than 20 cm, and the relative root mean square errors for the estimates of both DBH and H were less than 5%. This implies that TLS is very promising for the retrieval of tree and stand parameters in forest stands. For the applications of these methods to mixed forests with a structure of multilayer canopies, further examination is needed.

Description: In this letter, a novel algorithm for attitude measurement based on a 3-D electromagnetic model (3-D em-model) is proposed. The 3-D em-model is established offline based on the geometric structure of the target, and it can be used to predict the scattering features at different target attitudes. In order to measure the attitude of the air target, we design a bistatic step frequency radar system. The directions of the two radars' lines of sight (LOSs) relative to the target are acquired by matching the high-resolution range profiles (HRRPs) from the target echoes to the HRRPs generated from the 3-D em-model. Since the directions of two radars' LOSs relative to the Earth are already known, the absolute attitude of the target can be acquired. The innovative contributions of this letter are as follows: 1) A comprehensive theoretical analysis of air target attitude measurement based on its own 3-D em-model is proposed; 2) the method can be applied to different kinds of air targets such as aircraft, satellite, missile, etc.; 3) the proposed attitude measurement method does not require target motion model in advance; and 4) the proposed algorithm can be applied to any kind of step frequency waveforms. Experiments using both data predicted by a high-frequency electromagnetic code and data measured in the chamber verify the validity of the method.

Description: The Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions provide Level-1 brightness temperature (Tb) observations that are used for global soil moisture estimation. However, the nature of these Tb data differs: the SMOS Tb observations contain atmospheric and select reflected extraterrestrial (“Sky”) radiation, whereas the SMAP Tb data are corrected for these contributions, using auxiliary near-surface information. Furthermore, the SMOS Tb observations are multiangular, whereas the SMAP Tb is measured at 40° incidence angle only. This letter discusses how SMOS Tb, SMAP Tb, and radiative transfer modeling components can be aligned in order to enable a seamless exchange of SMOS and SMAP Tb data in soil moisture retrieval and assimilation systems. The aggregated contribution of the atmospheric and reflected Sky radiation is, on average, about 1 K for horizontally polarized Tb and 0.5 K for vertically polarized Tb at 40° incidence angle, but local and short-term values regularly exceed 5 K.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Description: How to produce the difference data of the two temporal images is a crucial factor in image change detection. In this letter, we propose multicontextual mutual information data (MMID) based on the bivariate Gaussian distribution (BGD) for synthetic aperture radar (SAR) image change detection and illustrate their superiorities over the classical difference data. MMID, which are an improved form of image spatial mutual information, are constructed based on the quadrilateral Markov random field (QMRF) and can be factored into the linear combination of the entropies. Then to adapt MMID to the change detection, we construct the 2-D entropies based on the BGD. In this way, MMID are able to capture the intertemporal statistical dependence of the two temporal images and thus can be taken as the feature-level difference data rather than the pixel-level data. The maximum-likelihood method, the automatic threshold method, and the Markov random field method are performed on the MMID of the real two temporal SAR images for the change detection. Experimental results demonstrate the superiorities of MMID over the traditional difference data.

Description: Spectral unmixing has been a popular technique for analyzing remotely sensed hyperspectral images. The goal of unmixing is to find a collection of pure spectral constituents (called endmembers ) that can explain each (possibly mixed) pixel of the scene as a combination of endmembers, weighted by their coverage fractions in the pixel or abundances . Over the last years, many algorithms have been presented to address the three main parts of the spectral unmixing chain: 1) estimation of the number of endmembers; 2) identification of the endmember signatures; and 3) estimation of the per-pixel fractional abundances. However, to date, there is no standardized tool that integrates these algorithms in a unified framework. In this letter, we present HyperMix, an open-source tool for spectral unmixing that integrates different approaches for spectral unmixing and allows building unmixing chains in graphical fashion, so that the end-user can define one or several spectral unmixing chains in fully configurable mode. HyperMix provides efficient implementations of most of the algorithms used for spectral unmixing, so that the tool automatically recognizes if the computer has a graphics processing unit (GPU) available and optimizes the execution of these algorithms in the GPU. This allows for the execution of spectral unmixing chains on large hyperspectral scenes in computationally efficient fashion. The tool is available online from http://hypercomphypermix.blogspot.com.es and has been validated with real hyperspectral scenes, providing state-of-the-art unmixing results.

Description: Seismic signals are nonlinear, and the seismic state-space model can be described as a nonlinear system. The particle filter (PF) method, as an effective method for estimating the state of a nonlinear system, can be applied to deal with seismic random noise attenuation. However, PF suffers from sample impoverishment caused by resampling, which results in serious loss of valid seismic information and leads to inaccurate representation of the reflected signal. To address the impoverishment issue and to further improve the particle quality, we propose a novel method to suppress seismic random noise—the adaptive fission particle filter (AFPF). In AFPF, all the particles undergo a fission process and produce “offspring” particles to maintain particle diversity. To implement the adaptation and to monitor the degree of fission, we apply a fission factor, which takes into account weights that indicate the quality of the particles. This leads to significant improvements in the particle quality, i.e., the proportion of highly weighted particles is increased. The effective seismic information provided by the resulting particles reproduces the true signal more reliably, reducing the bias of PF. In addition, we establish a dynamic state-space model suitable for seismic signals. Experimental results on synthetic records and field data illustrate the superior performance of AFPF in noise attenuation and reflected signal preservation compared with the PF.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Description: The primary objective of the TanDEM-X mission is the generation of a global high-precision digital elevation model (DEM) by using synthetic aperture radar interferometry. This letter presents the developed strategy for estimating the relative height error of the TanDEM-X DEM on a global scale. The mosaicking process of the final DEM combines all acquisitions at full resolution and is expected to be finished by late 2016. On the other hand, global mosaics can be generated starting from quicklook images already available for each single input data take. These downsized mosaics are operationally used to analyze the performance improvement that can be achieved by combining multiple acquisitions over the same ground areas and are a powerful mean for optimizing further acquisition planning. This letter reports the expected global performance of the final TanDEM-X product in advance of the full-resolution DEM. Knowledge of the global status of the TanDEM-X DEM relative height error is fundamental for optimizing the acquisition strategy and, therefore, the final performance and represents a valuable input for the scientific community as well as for selecting suitable areas for further interferometric experiments on a global scale.

Description: Support vector machines (SVMs) have been applied to land cover classification, and a number of studies have demonstrated their ability to increase classification accuracy. The high correlation between the data set and SVM training model parameters indicates the high performance of the classification model. To improve the correlation, research has focused on the integration of SVMs and other algorithms for data set selection and SVM training model parameter estimation. This letter proposes a novel method, based on a particle filter (PF), of estimating SVM training model parameters according to an observation system. By treating the SVM training function as the observation system of the PF, the new method automatically updates the SVM training model parameters to values that are more appropriate for the data set and can provide a better classification model than can the original model, wherein the parameters are set by trial and error. Various experiments were conducted using Radarsat-2 synthetic aperture radar data from the 2011 Thailand flood. The proposed method provides superior performance and a more accurate analysis compared with the standard SVM.

Description: Maize is a widely planted crop in China and in other areas of the world and plays an important role in grain production. Monitoring the growth status of maize using remote sensing technology is an important component of precision agriculture and height, as a crucial growth indicator for maize, can be retrieved from light detection and ranging (LIDAR) data. However, height extraction for crops, such as maize using airborne laser scanning point clouds results in a great number of uncertainties and challenges. Here, airborne full-waveform LIDAR data were used to extract maize height. In the first step, a workflow was designed based on the Gold deconvolution algorithm combined with a basic data process technique. The method was then tested and was determined to be effective for capturing the portion of the waveform interacting with the tops of vegetation, characterized by lower amplitude stemming from the ground. Therefore, the number of second returns from point clouds was dramatically increased. During the experiment, the number of point clouds increased nearly 50% for three of the four maize plots, as compared with the original point clouds. Compared with the commonly used Gaussian fitting algorithm, the deconvolution algorithm had the advantage of extracting an accurate position for overlapping weak signals. The height percentiles indicated that the original and Gaussian decomposition derived point clouds data underestimated and deconvolution algorithm can accurately reflect the true height of maize, particularly for the 75% and 95% height percentiles.

Description: The Moderate Resolution Imaging Spectroradiometer (MODIS) is an important data set in global burned-area mapping. The MODIS global burned-area product has a coarse spatial resolution at approximately 500 m, which often introduces errors to the mapped burned areas. In this letter, a novel subpixel mapping (SPM) approach was proposed to produce burned-area maps at the fine spatial resolution similar to Landsat imagery, by exploring the spectral and spatial information provided by the second and fifth bands of MODIS. The proposed SPM approach aims to refine the estimate of burned areas, which have been detected by the MODIS global burned-area product. The performance of the proposed SPM approach was assessed with an experiment area containing six burned areas, by comparing with the MODIS burned-area product MCD45. The result shows that the average omission error decreased from 52.26% for MCD45 to 16.74% for SPM, and the average commission error decreased from 21.76% for MCD45 to 12.54% for SPM. The kappa value increased from 0.5583 for MCD45 to 0.8756 for SPM, indicating that the proposed SPM approach is effective in reducing the influence of the coarse spatial resolution of MODIS imagery in mapping a burned area and refining existing global burned-area products.

Description: Radiometric distortions caused by rugged terrain make the classification of forest types from satellite imagery a challenge. Various band-specific topographic normalization models are expected to eliminate or reduce these effects. The quality of these models also depends on the approach to estimate empirical parameters. Generally, a global estimation of these parameters from a whole satellite image is simple, but it may tend to overcorrection, particularly for larger areas. A land-cover-specific method usually performs better, but it requires obtaining a priori land classification, which presents another challenge in many cases. Empirical parameters can be directly estimated from local pixels in a given window. In this letter, we propose and evaluate a central-pixel-based parameter estimation method for topographic normalization using local window pixels. We tested the method with Landsat 8 imagery and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) in very rough terrain with diverse forest types. Visual comparison and statistical analyses showed that the proposed method performed better at a range of window sizes compared with an uncorrected image or with a global parameter estimation approach. The intraclass spectral variability of each forest type has been reduced significantly, and it can yield higher accuracy of forest type classification. The proposed method does not require the a priori knowledge of land covers. Its simplicity and robustness suggest that this method has the potential to be a standard preprocessing approach for optical satellite imagery, particularly for rough terrain.

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

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

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

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

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

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

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

Description: Many methods have been developed to detect damaged buildings due to earthquake. However, little attention has been paid to analyze slightly affected buildings. In this letter, an unsupervised method is presented to detect earthquake-triggered “ roof-holes ” on rural houses from unmanned aerial vehicle (UAV) images. First, both orthomosaic and gradient images are generated from a set of UAV images. Then, a modified Chinese restaurant franchise model is used to learn an unsupervised model of the geo-object classes in the area by fusing both oversegmented orthomosaic and gradient images. Finally, “roof-holes” on rural houses are detected using the learned model. The performance of the proposed method is evaluated in terms of both qualitative and quantitative indexes.

Description: Automatic target generation process (ATGP) has been used in a wide range of applications in hyperspectral image analysis. It performs a sequence of orthogonal subspace projections to extract potential targets of interest. This letter presents a recursive version of the ATGP, which is referred to as the recursive ATGP (RATGP) and has three advantages over the ATGP as follows: 1) there is no need of inverting a matrix as the ATGP does for finding each new target; 2) there is a significant reduction in the computational complexity in the hardware design due to its recursive structure; and 3) there is an automatic stopping rule that can be derived by the Neyman–Pearson detection theory to terminate the algorithm.

Description: In this letter, we propose a novel automatic algorithm for road extraction from remote sensing images. The algorithm includes low- and high-level processing. In the low-level processing, we determine a normalized second derivative map of road profiles of a generalized bar shape, which is width invariant and contrast proportional, and accordingly obtain initial road center pixels. In the high-level processing, using the map and initial center pixels, we initially determine road segments. The segments are then locally refined using their orientation randomness and length-to-width ratio and further refined via global graph-cut optimization. A final road network is thereby extracted in a robust manner. Experimental results demonstrate that the proposed algorithm provides noticeably more robust and higher road extraction performance in various images compared with the existing algorithms.

Description: Remote sensing images often need to be coded and/or transmitted with constrained computational resources. Among other features, such images commonly have high spatial, spectral, and bit-depth resolution, which may render difficult their handling. This letter introduces an embedded quantization scheme based on two-step scalar deadzone quantization (2SDQ) that enhances the quality of transmitted images when coded with a constrained number of bits. The proposed scheme is devised for use in JPEG2000. It is named cell-based 2SDQ since it uses cells, i.e., small sets of wavelet coefficients within the codeblocks defined by JPEG2000. Cells permit a finer discrimination of coefficients in which to apply the proposed quantizer. Experimental results indicate that the proposed scheme is especially beneficial for high bit-depth hyperspectral images.

Description: High suspended solid (SS) concentrations in coastal waters are aesthetically undesirable, and adversely affect fisheries and coastal ecosystems. Environmental agencies usually require frequent measurements of SS over coastal regions at a spatially detailed level for water quality assessment and control. To develop a method for SS estimation in the complex coastal waters of Hong Kong, an archive of 57 Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and HJ-1 A/B Charged Couple Device (CCD) images over a 13-year period from January 2000 to December 2012 was used. Atmospherically corrected Landsat TM/ETM+ and HJ-1 A/B CCD bands 1–4 along with 240 in situ field samples of SS concentration collected within 2 h of image acquisition, were used to develop and validate regression models over a wide range of SS concentrations from 0.5–56.0 mg/L. The best representation of actual SS concentrations was given by the log-transformed combination of Band 2 (Green, 0.52–0.60 $mumbox{m}$ ) and Band 3 (Red, 0.63–0.69 $mumbox{m} $ ), with correlation coefficient (R) of 0.85, root-mean-square error of 2.60 mg/L and mean absolute error of 2.04 mg/L. This is attributed to the sensitivity of SS to green and red wavelengths specific to the characteristic refractive index and grain size of SS found in Hong Kong waters. This letter is considered more robust than previous studies, due to the much larger number of images and in situ samples used for model development and validation, as well as the different times of year and wide range of SS concentrations investigated.

Description: The quantitative estimation of the fractional cover of carbonate rock (CR) is critical for natural resource management and ecological conservation in karst areas. Based on the analysis of spectral properties of CR together with other land cover types, we proposed two CR indices (CRIs) and established the model that represents the relationships between the CRIs and the fractional cover of CR. Then, the fractional cover of CR was estimated by using the developed model. Experimental results on Landsat-8 Operational Land Imager images acquired at Southwestern China demonstrated the effectiveness of the developed model. Compared with other indices, the proposed CRIs show the highest correlations with the fractional cover of CR.

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