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TOC Alert for Publication# 8859 2017December 11<![CDATA[Front Cover]]>1412C1C1528<![CDATA[IEEE Geoscience and Remote Sensing Letters publication information]]>1412C2C290<![CDATA[Table of contents]]>141221732467321<![CDATA[Imaging Navigation and Registration for Geostationary Imager]]>1412217521796674<![CDATA[Automated Detection of Selective Logging Using SmallSat Imagery]]>1412218021842917<![CDATA[An Approach to Classify Tall Vegetation and Urban Using Deoriented PALSAR Image]]>141221852189908<![CDATA[Identification of Soybean Foliar Diseases Using Unmanned Aerial Vehicle Images]]>1412219021941887<![CDATA[Efficient ISAR Phase Autofocus Based on Eigenvalue Decomposition]]>1412219521992138<![CDATA[Power Transmission Tower Detection Based on Polar Coordinate Semivariogram in High-Resolution SAR Image]]>1412220022041762<![CDATA[Infinite Number of Looks Prediction in SAR Filtering by Linear Regression]]>1412220522096396<![CDATA[Estimation of Microwave Atmospheric Transmittance Over China]]>14122210221411872<![CDATA[Sensitivity of Texture Parameters to Acoustic Incidence Angle in Multibeam Backscatter]]>1412221522192058<![CDATA[Sparsity Regularized Nonlinear Inversion for Microwave Imaging]]>1412222022241578<![CDATA[A New Nonlinear Chirp Scaling Algorithm for High-Squint High-Resolution SAR Imaging]]>1412222522292857<![CDATA[An Over-Complete Dictionary Design Based on GSR for SAR Image Despeckling]]>1412223022341560<![CDATA[Joint Sparse Tensor Representation for the Target Detection of Polarized Hyperspectral Images]]>1412223522392574<![CDATA[Lobbes: An Algorithm for Sparse-Spike Deconvolution]]>Lobbes (Lasso-based binary search for parameter selection). It improves the fast iterative shrinkage and threshold algorithm for Toeplitz-sparse matrix factorization by performing three steps to find a suitable regularization parameter: 1) a normalization procedure over the input data; 2) a binary search step based on the least absolute shrinkage and selection operator; and 3) the elimination of consecutive peaks similar to non-maximum suppression. Such parameter allows us to find a solution with a specified sparsity. We compare our results against the original algorithm and with the known sparse-inducing greedy approach of orthogonal matching pursuit. Relative to state-of-the-art, results demonstrate that Lobbes generates better results: better signal-to-noise ratio of the reconstructed signal and better result for reflectivity peaks. We also derive a new way to measure the quality of the deconvolution.]]>1412224022441094<![CDATA[Zero-Shot Learning of SAR Target Feature Space With Deep Generative Neural Networks]]>1412224522491511<![CDATA[Encoding Spectral and Spatial Context Information for Hyperspectral Image Classification]]>1412225022542197<![CDATA[Haze Correction for Contrast-Based Multispectral Pansharpening]]>1412225522591272<![CDATA[Effective Scattering Albedo of Forests Retrieved by SMOS and a Three-Parameter Algorithm]]>141222602264836<![CDATA[A Super-Resolution Computational Coincidence Imaging Method Based on SIMO Radar System]]>141222652269658<![CDATA[A Two-Stage Detector for Mismatched Subspace Signals]]>141222702274500<![CDATA[Remote Sensing Image Denoising Using Patch Grouping-Based Nonlocal Means Algorithm]]>1412227522792380<![CDATA[Range Migration Algorithm for Near-Field MIMO-SAR Imaging]]>1412228022841231<![CDATA[Quantification of Temporal Decorrelation in X-, C-, and L-Band Interferometry for the Permafrost Region of the Qinghai–Tibet Plateau]]>1412228522892747<![CDATA[A Novel Two-Step Approach of Error Estimation for Stepped-Frequency MIMO-SAR]]>1412229022942711<![CDATA[Image Fusion of Spectrally Nonoverlapping Imagery Using SPCA and MTF-Based Filters]]>1412229522993656<![CDATA[Influence of Surface Roughness Sample Size for C-Band SAR Backscatter Applications on Agricultural Soils]]>$s$ and ${l}$ depending on the number of 1-m-long profiles measured per field; 2) computing the correlation of field average roughness parameters with backscatter observations; and 3) evaluating the goodness of fit of three widely used backscatter models, i.e., integral equation model (IEM), geometrical optics model (GOM), and Oh model. The results obtained illustrate a different behavior of the two roughness parameters. A minimum of 10–15 profiles can be considered sufficient for an accurate determination of $s$ , while 20 profiles might still be not enough for accurately estimating ${l}$ . The correlation analysis revealed a clear sensitivity of backscatter to surface roughness. For sample sizes >15 profiles, ${R}$ values were as high as 0.6 for ${s}$ and ~0.35 for ${l}$ , while for smaller sample sizes ${R}$ values dropped significantly. Similar results were obtained when applying the backscatter models, with enhanced model precision for larger sample sizes. However, IEM and GOM results were poorer than those obtained with the Oh model and more affected by lower sample sizes, probably due to larger uncertainly of ${l}$ .]]>141223002304808<![CDATA[Classification of High-Resolution Remote-Sensing Image Using OpenStreetMap Information]]>1412230523092251<![CDATA[Generative Adversarial Networks for Change Detection in Multispectral Imagery]]>1412231023143754<![CDATA[On the Relationship Between Wind, SST, and the Thermocline in the Seychelles–Chagos Thermocline Ridge]]>1412231523191197<![CDATA[Hyperspectral Band Selection via Rank Minimization]]>141223202324613<![CDATA[Deep Fully Convolutional Networks for the Detection of Informal Settlements in VHR Images]]>14122325232915737<![CDATA[Dictionary Learning-Based Hough Transform for Road Detection in Multispectral Image]]>$L_{1}$ -norm regularization as a case study and conduct extensive experiments on RSSCN7 data set to verify the proposed algorithm. The experimental results demonstrate the superiority of our method in comparison with traditional methods.]]>1412233023342348<![CDATA[Automatic Hyperspectral Image Restoration Using Sparse and Low-Rank Modeling]]>$ell _{1}$ penalized least squares for estimating the unknown signal. The Stein’s unbiased risk estimator is exploited to select all the parameters of the model yielding a fully automatic (parameter free) technique. Experimental results confirm that HyRes outperforms the state-of-the-art techniques in terms of signal-to-noise ratio, structural similarity index, and spectral angle distance for a simulated data set and in terms of noise-level estimation for the real data sets used in this letter. In the experiments, it was noted that HyRes is computationally less expensive compared with competitive techniques. Therefore, HyRes can be used as a reliable automatic preprocessing step for further analysis of HSIs.]]>141223352339974<![CDATA[Seasonal Snow Cover Change Detection Over the Indian Himalayas Using Polarimetric SAR Images]]>$(hh{-}vv)$ correlation coefficient and the total scattering power. This ratio provides a very efficient index for snow characterization. The difference image is obtained by temporal (winter–summer) ratioing of this index. The snow cover map is obtained by thresholding the difference image using the standard method of Otsu. The proposed algorithm is validated using the temporal RADARSAT-2 (FQ-28) C-band full-polarimetric synthetic aperture radar data sets acquired over the Manali–Dhundi region of Himachal Pradesh, India. The results are explicitly validated with in situ observatory measurements and compared with the normalized difference snow index-based snow cover maps derived from the LANDSAT-8 optical satellite images.]]>1412234023442846<![CDATA[Sensitivity of Summer Drying to Spring Snow-Albedo Feedback Throughout the Northern Hemisphere From Satellite Observations]]>1412234523494577<![CDATA[Study on Typhoon Center Monitoring Based on HY-2 and FY-2 Data]]>1412235023545337<![CDATA[Hyperspectral Images Classification With Gabor Filtering and Convolutional Neural Network]]>1412235523592122<![CDATA[Tree Classification in Complex Forest Point Clouds Based on Deep Learning]]>1412236023644168<![CDATA[Hyperspectral Band Selection Based on Deep Convolutional Neural Network and Distance Density]]>141223652369628<![CDATA[Suitability of Data Representation Domains in Expressing Human Motion Radar Signals]]>141223702374707<![CDATA[Accurate and Efficient Simulation Model for the Scattering From a Ship on a Sea-Like Surface]]>1412237523791402<![CDATA[High-Resolution Wide-Swath Imaging of Spaceborne Multichannel Bistatic SAR With Inclined Geosynchronous Illuminator]]>1412238023841393<![CDATA[Landsat-8 TIRS Data for Assessing Urban Heat Island Effect and Its Impact on Human Health]]>141223852389795<![CDATA[Focusing Nonparallel-Track Bistatic SAR Data Using Extended Nonlinear Chirp Scaling Algorithm Based on a Quadratic Ellipse Model]]>1412239023941153<![CDATA[Prediction of Subsurface NMR T2 Distributions in a Shale Petroleum System Using Variational Autoencoder-Based Neural Networks]]>1H and ^{13}C nuclei. Subsurface NMR measurements are generally acquired as well logs that provide information about fluid mobility and fluid-filled pore size distribution. Acquisition of subsurface NMR log is limited due to operational and instrumentation challenges. We implement a variational autoencoder (VAE) for improved training of a neural network (NN) to generate the NMR-T2 distributions along a 300-ft depth interval in a shale petroleum system at 11000-ft depth below sea level. Subsurface mineral and kerogen volume fractions, fluid saturations, and T2 distributions acquired at 460 discrete depth points were used as the training data set. The trained VAE-NN successfully predicts the T2 distributions for 100 discrete depths at an $R^{2}$ of 0.75 and normalized root-mean-square deviation of 15%.]]>141223952397893<![CDATA[Video Satellite Imagery Super Resolution via Convolutional Neural Networks]]>1412239824021889<![CDATA[Target Recognition in Radar Images Using Weighted Statistical Dictionary-Based Sparse Representation]]>141224032407825<![CDATA[Multispectral Misregistration of Sentinel-2A Images: Analysis and Implications for Potential Applications]]>141224082412980<![CDATA[Low-Rank Model for Wideband Electromagnetic Induction Sensors]]>141224132417915<![CDATA[Change Detection Based on Deep Features and Low Rank]]>1412241824223479<![CDATA[Improving Burst Alignment in TOPS Interferometry With Bivariate Enhanced Spectral Diversity]]>1412242324273746<![CDATA[Wind and Current Dependence of the First-Order Bragg Scattering Power in High-Frequency Radar Sea Echoes]]>1412242824321588<![CDATA[A New Saliency-Driven Fusion Method Based on Complex Wavelet Transform for Remote Sensing Images]]>1412243324374345<![CDATA[Group Lasso-Based Band Selection for Hyperspectral Image Classification]]>141224382442643<![CDATA[Sea—Land Segmentation for Panchromatic Remote Sensing Imagery via Integrating Improved MNcut and Chan—Vese Model]]>1412244324471984<![CDATA[Classification of Multisensor and Multiresolution Remote Sensing Images Through Hierarchical Markov Random Fields]]>1412244824522371<![CDATA[On the Errors in Randomly Sampled Nonsparse Signals Reconstructed With a Sparsity Assumption]]>141224532456238<![CDATA[Compressive Sensing of Hyperspectral Images via Joint Tensor Tucker Decomposition and Weighted Total Variation Regularization]]>1412245724611423<![CDATA[Deep Neural Network Initialization Methods for Micro-Doppler Classification With Low Training Sample Support]]>1412246224661698<![CDATA[Introducing IEEE Collabratec]]>1412246724672255<![CDATA[IEEE Geoscience and Remote Sensing Letters information for authors]]>1412C3C362<![CDATA[IEEE Geoscience and Remote Sensing Letters Institutional Listings]]>1412C4C4314