<![CDATA[ IEEE Transactions on Geoscience and Remote Sensing - new TOC ]]>
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TOC Alert for Publication# 36 2019September05<![CDATA[Front Cover]]>579C1C1493<![CDATA[IEEE Transactions on Geoscience and Remote Sensing publication information]]>579C2C2124<![CDATA[Table of contents]]>57962577304126<![CDATA[The Parallel SBAS Approach for Sentinel-1 Interferometric Wide Swath Deformation Time-Series Generation: Algorithm Description and Products Quality Assessment]]>5796259628133472<![CDATA[Estimation of Source Wavelet From Seismic Traces Using Groebner Bases]]>579628262912149<![CDATA[A Shadowing Mitigation Approach for Sea State Parameters Estimation Using X-Band Remotely Sensing Radar Data in Coastal Areas]]>in situ measurements as external references. To determine the threshold value for the interpolation approach, the influence of the antenna height on shadowing modulation effects is examined through performing an analysis of variance (ANOVA) that uses data from two X-band radars deployed at 10 and 20 m above MSL. ANOVA results reveal that it is possible to explain the increment of intensities affected by shadowing throughout the distance using an adaptive threshold retrieved from a third-order polynomial function of the mean radar cross section (RCS). Finally, an X-band radar is installed at 13 m above MSL to test the proposed technique. During measurements, the wind and wave conditions varied, and the antenna-look direction remained constant. Errors for $H_{s}$ , $theta _{p}$ , and $T_{p}$ calculated as the difference between estimated and true data show a mean bias and a relative value of 0.05 m (2.72%), 1.52° (5.94%), and 0.15 s (1.67%), respectively. The directional and wave energy spectra derived from radar estimates, acoustic wave and current, ADVs record, as well as JONSWAP formulation are presented to illustrate the i-
provement resulting from the proposed method over the frequency domain.]]>579629263107264<![CDATA[A Transverse Spectrum Deconvolution Technique for MIMO Short-Range Fourier Imaging]]>5796311632417838<![CDATA[Effects of Wind Wave Spectra on Radar Backscatter From Sea Surface at Different Microwave Bands: A Numerical Study]]>579632563343223<![CDATA[OS-Flow: A Robust Algorithm for Dense Optical and SAR Image Registration]]>57963356354113952<![CDATA[MODIS Reflective Solar Bands On-Orbit Calibration and Performance]]>$2.2~mu text{m}$ collect data at three nadir spatial resolutions: 250 m, 500 m, and 1 km. The solar diffuser (SD) coupled with the SD stability monitor (SDSM) provides a reflectance-based calibration on-orbit. In addition, lunar observations and response trends from pseudoinvariant desert sites are used to characterize the response versus scan-angle changes on-orbit. This paper provides a brief overview of MODIS RSB calibration algorithms, as implemented in the latest Level 1B version 6.1, operational activities, on-orbit performance, remaining challenges, and potential improvements. Results from the SD and SDSM measurements show a wavelength and mirror-side-dependent degradation in RSB responses, with the largest degradation at the shortest wavelengths, particularly for Terra MODIS. Aqua MODIS has experienced far less degradation of its optics and on-board calibrators compared with Terra MODIS, resulting in an overall better performance. With the exception of Aqua band 6, there have been no new noisy or inoperable detectors in the RSB of either instrument during postlaunch operations. As the instruments age and continue to endure the space environment, the detectors and the optical systems degrade. The challenges associated wit-
incorporating these on-orbit changes to ensure a production of high-quality calibrated L1B data products are also discussed in this paper.]]>579635563715032<![CDATA[High-Frequency Ionospheric Monitoring System for Over-the-Horizon Radar in Canada]]>579637263844028<![CDATA[A Ship ISAR Imaging Algorithm Based on Generalized Radon-Fourier Transform With Low SNR]]>579638563965014<![CDATA[CoSMIR Performance During the GPM OLYMPEX Campaign]]>579639764075024<![CDATA[Sentinel-2 Sharpening Using a Reduced-Rank Method]]>5796408642012679<![CDATA[Robust Band-Dependent Spatial-Detail Approaches for Panchromatic Sharpening]]>579642164332834<![CDATA[Full-Polarization Bistatic Scattering From an Inhomogeneous Rough Surface]]>579643464466422<![CDATA[Ship Detection Based on Complex Signal Kurtosis in Single-Channel SAR Imagery]]>579644764615010<![CDATA[Multiscale Locality and Rank Preservation for Robust Feature Matching of Remote Sensing Images]]>$K$ Rank Preservation (mTopKRP) for robust feature matching. To this end, we first search the $K$ -nearest neighbors of each feature point and generate a ranking list accordingly. Then we design a metric based on the weighted Spearman’s footrule distance to describe the similarity of two ranking lists specifically for the matching problem. We build a mathematical optimization model and derive its closed-form solution, enabling our method to establish reliable correspondences in linearithmic time complexity, which requires only tens of milliseconds to handle over 1000 putative matches. We also introduce a multiscale strategy for neighborhood construction, which increases the robustness of our method and can deal with different types of degradation, even when the image pair suffers from a large scale change, rotation, nonrigid deformation, or a large number of mismatches. Extensive experiments on several representative remote sensing image data sets demonstrate the superiority of our method over state of the art.]]>579646264723845<![CDATA[Identification of Sun Glint Contamination in GMI Measurements Over the Global Ocean]]>5796473648312605<![CDATA[Guided Patchwise Nonlocal SAR Despeckling]]>5796484649810474<![CDATA[An Analytic Expression for the Phase Noise of the Goldstein–Werner Filter]]>579649965165299<![CDATA[Learning and Adapting Robust Features for Satellite Image Segmentation on Heterogeneous Data Sets]]>5796517652912684<![CDATA[Hydra: An Ensemble of Convolutional Neural Networks for Geospatial Land Classification]]>https://github.com/maups/hydra-fmow.]]>579653065417080<![CDATA[Characterizing the System Impulse Response Function From Photon-Counting LiDAR Data]]>579654265513850<![CDATA[<italic>StfNet</italic>: A Two-Stream Convolutional Neural Network for Spatiotemporal Image Fusion]]>StfNet. The novelty of this paper is twofold. First, considering the temporal dependence among image sequences, we incorporate the fine image acquired at the neighboring date to super-resolve the coarse image at the prediction date. In this way, our network predicts a fine image not only from the structural similarity between coarse and fine image pairs but also by exploiting abundant texture information in the available neighboring fine images. Second, instead of estimating each output fine image independently, we consider the temporal relations among time-series images and formulate a temporal constraint. This temporal constraint aiming to guarantee the uniqueness of the fusion result and encourages temporal consistent predictions in learning and thus leads to more realistic final results. We evaluate the performance of the StfNet using two actual data sets of Landsat-Moderate Resolution Imaging Spectroradiometer (MODIS) acquisitions, and both visual and quantitative evaluations demonstrate that our algorithm achieves state-of-the-art performance.]]>579655265647015<![CDATA[Learning Temporal Features for Detection on Maritime Airborne Video Sequences Using Convolutional LSTM]]>579656565768320<![CDATA[A CIE Color Purity Algorithm to Detect Black and Odorous Water in Urban Rivers Using High-Resolution Multispectral Remote Sensing Images]]>$R_{mathrm {rs}}$ ) in visible bands is 25.19%. We first measured $R_{mathrm {rs}}$ spectra of two classes of BOW [BOW with high concentrations of iron (II) sulfide, i.e., BOW1 and BOW with high concentrations of total suspended matter, i.e., BOW2] and ordinary water in Shenyang. Then, in situ$R_{mathrm {rs}}$ data were converted into $R_{mathrm {rs}}$ corresponding to the wide GF-2 bands using the spectral response functions. We used the converted $R_{mathrm {rs}}$ data to calculate several band combinations, including the baseline height, [$R_{mathrm {rs}}$ (green) $- R_{mathrm {rs}}$ (red))/($R_{mathrm {rs}}$ (green) $+ R_{mathrm {rs}}$ (red)], and the color purity on a Commission Internationale de L’Eclairage (CIE) chromaticity diagram. The color pur-
ty was found to be the best index to extract BOW from ordinary water. Then, $R_{mathrm {rs}}$ (645) was applied to categorize BOW into BOW1 and BOW2. We applied the algorithm to two synchronous GF-2 images. The recognition accuracy of BOW2 and ordinary water are both 100%. The extracted river water type near Weishanhu Road was BOW1, which agreed well with ground truth. The algorithm was further applied to other GF-2 data for Shenyang and Beijing.]]>579657765905548<![CDATA[Geosynchronous SAR Tomography: Theory and First Experimental Verification Using Beidou IGSO Satellite]]>579659166077204<![CDATA[Polarized Backscattering From Spatially Anisotropic Rough Surface]]>579660866184298<![CDATA[First Demonstration of Joint Wireless Communication and High-Resolution SAR Imaging Using Airborne MIMO Radar System]]>5796619663210617<![CDATA[Blind Hyperspectral Unmixing Considering the Adjacency Effect]]>5796633664916967<![CDATA[Spectrum Recovery for Clutter Removal in Penetrating Radar Imaging]]>579665066655246<![CDATA[Performance of POLYMER Atmospheric Correction of Ocean Color Imagery in the Presence of Absorbing Aerosols]]>$R_{mathrm {rs}}$ , sr^{−1}) in blue wavelengths in the presence of absorbing aerosols. Addressing this issue requires realistic absorbing-aerosol model and knowledge of the vertical distribution of aerosols, which are currently difficult to achieve. An alternative atmospheric correction approach has been evaluated in this paper for Moderate Resolution Imaging Spectroradiometer (MODIS) data. The approach is based on a previously developed spectra-matching optimization [POLYnomial-based approach established for the atmospheric correction of MERIS data (POLYMER)], where polynomial functions are used to express atmospheric contribution to the measured radiance and where a bio-optical model is used to estimate the water contribution. Evaluation against in situ data measured over the regions frequently affected by absorbing aerosols indicates that, compared with the NSAC approach, the POLYMER approach improves the $R_{mathrm {rs}}$ retrievals in blue wavelengths while having a slightly worse performance in other wavelengths. Evaluation using NSAC-retrieved $R_{mathrm {rs}}$ in adjacent days free of absorbing aerosols suggests that the POLYMER approach could improve the spectral shape and increase valid spatial coverage. When applied to time-series MODIS data, the POLYMER approach could generate more te-
porary coherent daily and monthly $R_{mathrm {rs}}$ patterns than the NSAC approach. These results suggest that the POLYMER approach could be an alternative approach to partly correct for absorbing aerosols in the absence of explicit information on the aerosol type and the vertical distribution.]]>5796666667427551<![CDATA[Multiple-Feature Kernel-Based Probabilistic Clustering for Unsupervised Band Selection]]>579667566897454<![CDATA[Deep Learning for Hyperspectral Image Classification: An Overview]]>5796690670929614<![CDATA[Phase Correlation Decomposition: The Impact of Illumination Variation for Robust Subpixel Remotely Sensed Image Matching]]>579671067259675<![CDATA[Observed Relationship Between BRF Spectral-Continuum Variance and Macroscopic Roughness of Clay Sediments]]>579672667403065<![CDATA[A Novel Inpainting Algorithm for Recovering Landsat-7 ETM+ SLC-OFF Images Based on the Low-Rank Approximate Regularization Method of Dictionary Learning With Nonlocal and Nonconvex Models]]>$left ({cdot }right)$ low-rank nonconvex model along with the dictionary. The algorithm was tested using the simulated ETM+ SLC-off images created from a multiband ETM+ SLC-on image file and compared to the high accuracy low-rank tensor completion (HaLRTC), logDet, and tensor nuclear norm (TNN) algorithms. The results show that the ETM+ images restored using the new algorithm have lower RMSE, higher PSNR and structure similarity (SSIM) values, and better visualization. These results indicate that the new algorithm performs better than the other three algorithms and can efficiently and accurately restore the data-missing stripes.]]>5796741675425016<![CDATA[Ionospheric Correction of InSAR Time Series Analysis of C-band Sentinel-1 TOPS Data]]>5796755677313342<![CDATA[Digital Terrain Model Retrieval in Tropical Forests Through P-Band SAR Tomography]]>579677467816670<![CDATA[An Adaptive <inline-formula> <tex-math notation="LaTeX">$L^{p}$ </tex-math></inline-formula>-Penalization Method to Enhance the Spatial Resolution of Microwave Radiometer Measurements]]>$L^{p}$ -minimization approach with a variable $p$ exponent. The algorithm automatically adapts the $p$ exponent to the region of the image to be reconstructed. This approach allows taking benefit of the advantages of both the regularization in Hilbert ($p = 2$ ) and Banach ($1< p< 2$ ) spaces. Experiments are undertaken considering the microwave radiometer and refer to both actual and simulated data collected by the special sensor microwave imager (SSM/I). Results demonstrate the benefits of the proposed method in reconstructing abrupt discontinuities and smooth gradients with respect to conventional approaches in Hilbert or Banach spaces.]]>579678267913267<![CDATA[Feature Fusion With Predictive Weighting for Spectral Image Classification and Segmentation]]>579679268076171<![CDATA[Unsupervised Spatial–Spectral Feature Learning by 3D Convolutional Autoencoder for Hyperspectral Classification]]>579680868208516<![CDATA[Continuous Human Motion Recognition With a Dynamic Range-Doppler Trajectory Method Based on FMCW Radar]]>579682168313851<![CDATA[A Global Adjustment Method for Photogrammetric Processing of Chang’E-2 Stereo Images]]>579683268434063<![CDATA[Simultaneous Mapping of Coastal Topography and Bathymetry From a Lightweight Multicamera UAS]]>5796844686414297<![CDATA[Prediction of Sea Ice Motion With Convolutional Long Short-Term Memory Networks]]>579686568765680<![CDATA[Subdictionary-Based Joint Sparse Representation for SAR Target Recognition Using Multilevel Reconstruction]]>579687768873071<![CDATA[Effect of Anisotropy on Ionospheric Scintillations Observed by SAR]]>4) index measured in SAR data pairs using two well-established techniques. The image contrast technique heavily relies on the accurate modeling of anisotropy, whereas the radar cross-sectional enhancement method is independent of it. This feature has been exploited in the $S_{4}$ comparison to finally fit the choice of irregularity axial ratio and conclude that the sheet-like-
structures best describe the ionospheric irregularity structure in the region under observation.]]>579688868992673<![CDATA[Preregistration Classification of Mobile LIDAR Data Using Spatial Correlations]]>5796900691545028<![CDATA[Scale-Free Convolutional Neural Network for Remote Sensing Scene Classification]]>579691669284251<![CDATA[A Large-Scale Multi-Institutional Evaluation of Advanced Discrimination Algorithms for Buried Threat Detection in Ground Penetrating Radar]]>2 of GPR data using surface area, from 13 different lanes across two U.S. test sites. The data were collected using a vehicle-mounted GPR system, the variants of which have supplied data for numerous publications. Using these results, we identify the most successful and common processing strategies among the submitted algorithms, and make recommendations for GPR-based BTD algorithm design.]]>579692969453630<![CDATA[A Terrestrial Validation of ICESat Elevation Measurements and Implications for Global Reanalyses]]>in situ survey.]]>579694669595873<![CDATA[Transferred Deep Learning-Based Change Detection in Remote Sensing Images]]>579696069735058<![CDATA[High-Speed Maneuvering Platforms Squint Beam-Steering SAR Imaging Without Subaperture]]>579697469853719<![CDATA[A Novel Approach to SAR Ocean Wind Retrieval]]>579698669955940<![CDATA[Airborne Circular W-Band SAR for Multiple Aspect Urban Site Monitoring]]>5796996701645030<![CDATA[One-Bit SAR Imaging Based on Single-Frequency Thresholds]]>5797017703218130<![CDATA[Fast 3-D Imaging Algorithm Based on Unitary Transformation and Real-Valued Sparse Representation for MIMO Array SAR]]>$ell _{2,1}$ -norm minimization model is established. In addition, a modification of the fast iterative shrinkage-thresholding algorithm (FISTA) is used to reconstruct the 3-D image for further improving the computational efficiency. Moreover, the theoretical analysis of computational complexity of the proposed algorithm is derived when compared with an existing complex domain algorithm. Finally, numerical simulations and MIMO array SAR real experimental results are illustrated to validate that the proposed algorithm can reduce the computational complexity significantly in terms of CPU time while still maintaining the inherent advantages of superresolution and robustness against the noise.]]>579703370474180<![CDATA[Automatic Design of Convolutional Neural Network for Hyperspectral Image Classification]]>579704870668502<![CDATA[Detection of Radio Frequency Interference in Microwave Radiometers Operating in Shared Spectrum]]>579706770748217<![CDATA[Assessments of Ocean Wind Retrieval Schemes Used for Chinese Gaofen-3 Synthetic Aperture Radar Co-Polarized Data]]>579707570856888<![CDATA[Seismic Phase Picking Using Convolutional Networks]]>$F_{1}$ -score of 93.13% for P phases and 91.07% for S phases. Our results show that convolutional networks are on track to achieve human-level performance on the task of seismic phase picking and can contribute to decreasing the need for manual analysis. An open-source implementation of the proposed approach, pretrained on the NCEDC data set, can be downloaded at https://github.com/stbnps/cospy.]]>579708670922713<![CDATA[Robust Target Detection Within Sea Clutter Based on Graphs]]>579709371032713<![CDATA[Infrared Small Target Detection Based on Facet Kernel and Random Walker]]>579710471183549<![CDATA[A Super-Resolution Sparse Aperture ISAR Sensors Imaging Algorithm via the MUSIC Technique]]>579711971342749<![CDATA[SMF-POLOPT: An Adaptive Multitemporal Pol(DIn)SAR Filtering and Phase Optimization Algorithm for PSI Applications]]>$times 7.2$ (the full-polarization case) and $times 3.8$ (the dual-polarization case) with respect to the classical full-resolution single-pol amplitude dispersion method, on the valid pixels’ densities. The excellent PolSAR filtering and ground deformation monitoring results achieved by the adaptive Pol(DIn)SAR filtering and phase optimization algorithm (i.e., the SMF-POLOPT) have validated the effectiveness of this proposed scheme.]]>5797135714720013<![CDATA[Two-Step Accuracy Improvement of Motion Compensation for Airborne SAR With Ultrahigh Resolution and Wide Swath]]>5797148716021748<![CDATA[Improvements in the Beam-Mismatch Correction of Precipitation Radar Data After the TRMM Orbit Boost]]>579716171691154<![CDATA[A CNN-Based Spatial Feature Fusion Algorithm for Hyperspectral Imagery Classification]]>579717071814376<![CDATA[Sparse Recovery on Intrinsic Mode Functions for the Micro-Doppler Parameters Estimation of Small UAVs]]>5797182719310432<![CDATA[SAR Speckle Nonlocal Filtering With Statistical Modeling of Haar Wavelet Coefficients and Stochastic Distances]]>5797194720821886<![CDATA[Road Detection and Centerline Extraction Via Deep Recurrent Convolutional Neural Network U-Net]]>579720972204564<![CDATA[Energy Flow Domain Reverse-Time Migration for Borehole Radar]]>5797221723114553<![CDATA[Caps-TripleGAN: GAN-Assisted CapsNet for Hyperspectral Image Classification]]>579723272457054<![CDATA[Structure-Aware Collaborative Representation for Hyperspectral Image Classification]]>579724672615708<![CDATA[High-Resolution Topography of Titan Adapting the Delay/Doppler Algorithm to the Cassini RADAR Altimeter Data]]>579726272683160<![CDATA[An Atmospheric Phase Screen Estimation Strategy Based on Multichromatic Analysis for Differential Interferometric Synthetic Aperture Radar]]>5797269728011658<![CDATA[Unmixing <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-Gaussians With Application to Hyperspectral Imaging]]>$K$ -Gaussians, given convex combinations of their realizations. In the remote sensing literature, this setting is known as the normal compositional model (NCM) and has shown promising gains in modeling hyperspectral images. Current NCM parameter estimation techniques are based on Bayesian methodology and are computationally slow and sensitive to their prior assumptions. Here, we introduce a deterministic variant of the NCM, named DNCM, which assumes that the unknown mixing coefficients are nonrandom. This leads to a standard Gaussian model with a simple estimation procedure, which we denote by $K$ -Gaussians. Its iterations are provided in closed form and do not require any sampling schemes or simplifying structural assumptions. We illustrate the performance advantages of $K$ -Gaussians using synthetic and real images, in terms of accuracy and computational costs in comparison to state of the art. We also demonstrate the use of our algorithm in hyperspectral target detection on a real image with known targets.]]>579728172934757<![CDATA[Effect of Microgeometry on Modeling Accuracy of Fluid-Saturated Rock Using Dielectric Permittivity]]>579729472992693<![CDATA[Comments on “The Influence of Equatorial Scintillation on L-Band SAR Image Quality and Phase”]]>57973007301424<![CDATA[Corrections to “FengYun-3 B Satellite Medium Resolution Spectral Imager Visible On-Board Calibrator Radiometric Output Degradation Analysis”]]>[1], the name of the institution of the authors Dandan Zhi, Tanqi Yu, and Yan Pan were incorrect, they are with the Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Key Laboratory of Optical Calibration and Characterization, Hefei 230031, China, and also with the University of Science and Technology of China, Hefei 230026, China (e-mail: 1416652331@qq.com).]]>5797302730273<![CDATA[IEEE Transactions on Geoscience and Remote Sensing information for authors]]>579C3C3117<![CDATA[IEEE Transactions on Geoscience and Remote Sensing institutional listings]]>579C4C4431