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TOC Alert for Publication# 36 2018February 22<![CDATA[[Front cover]]]>562C1C1697<![CDATA[IEEE Transactions on Geoscience and Remote Sensing publication information]]>562C2C287<![CDATA[Table of contents]]>5626091216200<![CDATA[Three-Dimensional Reconstruction From a Multiview Sequence of Sparse ISAR Imaging of a Space Target]]>5626116208673<![CDATA[LRAGE: Learning Latent Relationships With Adaptive Graph Embedding for Aerial Scene Classification]]>5626216344511<![CDATA[Low-Frequency SAR Radiometric Calibration and Antenna Pattern Estimation by Using Stable Point Targets]]>5626356464651<![CDATA[Improved Estimation for Well-Logging Problems Based on Fusion of Four Types of Kalman Filters]]>$U$ , $Th$ , and $K$ . Four types of Kalman filters are designed to estimate the elements using the NGT sensor. Then, a fusion of the Kalman filters is utilized into an integrated framework by an ordered weighted averaging (OWA) operator to enhance the quality of the estimations. A real covariance of the output error based on the innovation matrix is utilized to design weighting factors for the OWA operator. The simulation studies indicate not only a reliable performance of the proposed method compared with the individual Kalman filters but also a better response in contrast with previous fusion methodologies.]]>5626476541305<![CDATA[Faraday Rotation Correction for SMAP and Soil Moisture Retrieval]]>5626556684585<![CDATA[Multiview Intensity-Based Active Learning for Hyperspectral Image Classification]]>content, coarseness, contrast, and directionality, and the smooth component from each pair is chosen as one single view. Second, we construct two multiview intensity-based query strategies for active learning. On the one hand, we exploit the intensity differences of multiple views along with the samples’ uncertainty to choose the most informative candidates. On the other hand, we consider the clustering distribution of all unlabeled samples, and query the most representative candidates in addition to the highly informative ones. Our experiments are performed on four benchmark hyperspectral image data sets. The obtained results show that the proposed MVAL framework can lead to better classification performance than the traditional, single-view active learning schemes. In addition, compared with the conventional disagree-based MVAL scheme, the proposed query selection strategies show competitive classification accuracy.]]>5626696803667<![CDATA[Satellite-Link Attenuation Measurement Technique for Estimating Rainfall Accumulation]]>5626816935381<![CDATA[Nonlinear Hyperspectral Unmixing Based on Geometric Characteristics of Bilinear Mixture Models]]>5626947149770<![CDATA[An Extended Moving Target Detection Approach for High-Resolution Multichannel SAR-GMTI Systems Based on Enhanced Shadow-Aided Decision]]>5627157296296<![CDATA[A Hybrid Method of SAR Speckle Reduction Based on Geometric-Structural Block and Adaptive Neighborhood]]>56273074837131<![CDATA[Local Binary Pattern-Based Hyperspectral Image Classification With Superpixel Guidance]]>5627497595257<![CDATA[Improvement of Reflection Detection Success Rate of GNSS RO Measurements Using Artificial Neural Network]]>5627607694118<![CDATA[Fine-Grained Object Recognition and Zero-Shot Learning in Remote Sensing Imagery]]>5627707793818<![CDATA[The Dual-Baseline Phase Unwrapping Correction Framework for the TanDEM-X Mission Part 1: Theoretical Description and Algorithms]]>5627807988593<![CDATA[Analysis of Cross-Borehole Pulse Radar Signatures on a Terminated Tunnel With Various Penetration Lengths]]>5627998075276<![CDATA[A Universal Destriping Framework Combining 1-D and 2-D Variational Optimization Methods]]>5628088226269<![CDATA[Airborne Gamma-Ray Spectroscopy for Modeling Cosmic Radiation and Effective Dose in the Lower Atmosphere]]>40K, ^{214}Bi, and ^{208}Tl photopeaks, which need to be subtracted in processing airborne gamma-ray data in order to estimate the potassium, uranium, and thorium abundances in the ground. Moreover, a calibration procedure has been carried out by implementing the CARI-6P and Excel-based program for calculating atmospheric cosmic ray spectrum dosimetry tools, according to which the annual cosmic effective dose to human population has been linearly related to the measured cosmic count rates.]]>56282383410855<![CDATA[Radar Propagation Experiment in the North Sea: The Sylt Campaign]]>5628358463953<![CDATA[Spectral–Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework]]>5628478586136<![CDATA[An Efficient Amplitude-Preserving Generalized S Transform and Its Application in Seismic Data Attenuation Compensation]]>5628598664365<![CDATA[Extended Random Walker for Shadow Detection in Very High Resolution Remote Sensing Images]]>5628678765333<![CDATA[A New Inversion Method Based on Distorted Born Iterative Method for Grounded Electrical Source Airborne Transient Electromagnetics]]>5628778872553<![CDATA[Processing of Long Integration Time Spaceborne SAR Data With Curved Orbit]]>5628889044075<![CDATA[Mixture WG $Gamma$ -MRF Model for PolSAR Image Classification]]>$Gamma$ model has been validated as an effective model for the characteristic of polarimetric synthetic aperture radar (PolSAR) data statistics. However, due to the complexity of natural scene and the influence of coherent wave, the WG$Gamma$ model still needs to be improved to fully consider the polarimetric information. Then, we propose the WG$Gamma$ mixture model (WG$Gamma$ MM) for PolSAR data to maintain the correlations among statistics in PolSAR data. To further consider the spatial-contextual information in PolSAR image classification, we propose a novel mixture model, named mixture WG$Gamma$ -Markov random field (MWG$Gamma$ -MRF) model, by introducing the MRF to improve the WG$Gamma$ MM model for classification. In each law of the MWG$Gamma$ -MRF model, the interaction term based on the edge penalty function is constructed by the edge-based multilevel-logistic model, while the likelihood term being constructed by the WG$Gamma$ model, so that each law of the MWG$Gamma$ -MRF model can achieve an energy function and has its contribution to the inference of attributive class. Then, the mixture energy function of the MWG$Gamma$ -MRF model has the fusion of the weig-
ted component, given the energy functions of every law. The mixture coefficient and the corresponding mean covariance matrix of the MWG$Gamma$ -MRF model are estimated by the expectation-maximization algorithm, while the parameters of the WG$Gamma$ model being estimated by the method of matrix log-cumulants. Experiments on simulated data and real PolSAR images demonstrate the effectiveness of the MWG$Gamma$ -MRF model and illustrate that it can provide strong noise immunity, get smoother homogeneous areas, and obtain more accurate edge locations.]]>5629059207729<![CDATA[Examining the Impact of a Crude Oil Spill on the Permittivity Profile and Normalized Radar Cross Section of Young Sea Ice]]>56292193625190<![CDATA[Multisource Remote Sensing Data Classification Based on Convolutional Neural Network]]>5629379494503<![CDATA[Large-Scale Remote Sensing Image Retrieval by Deep Hashing Neural Networks]]>5629509656558<![CDATA[Analysis of Data Acquisition Time on Soil Moisture Retrieval From Multiangle L-Band Observations]]>3·m^{-3} for single-SM retrievals is achievable irrespective of the 6 A.M. and 6 P.M. overpass acquisition times for moisture conditions ≤0.15 m^{3}·m^{-3}. Additional tests on the use of the air temperature as proxy for the vegetation temperature also showed no preference for the acquisition time. The performance of multiparameter retrievals of SM and an additional parameter proved to be satisfactory for SM modeling-independent of the acquisition time-with root-mean-square errors less than 0.06 m^{3}·m^{-3} for the focus farm.]]>5629669713651<![CDATA[Model-Based Target Scattering Decomposition of Polarimetric SAR Tomography]]>5629729833985<![CDATA[A Regression-Based High-Pass Modulation Pansharpening Approach]]>5629849962749<![CDATA[Ensemble of ESA/AATSR Aerosol Optical Depth Products Based on the Likelihood Estimate Method With Uncertainties]]>562997100724969<![CDATA[Calibration Algorithm for Cross-Track Infrared Sounder Full Spectral Resolution Measurements]]>−1 for all the three bands, a new calibration algorithm has been developed and implemented for operational uses. The algorithm is an improvement over the previous algorithm that had been operationally used until March 2017. Major changes include the calibration equation, self-apodization correction and resampling matrices, and calibration filter. Compared to the previous algorithm, the improvement reduces the calibration inconsistencies among the nine fields of view and between the forward and reverse interferometer sweep directions by up to 0.5 K, and the differences between observed and simulated spectra by up to 0.4 K.]]>562100810161563<![CDATA[A Clustering Approach for the Detection of Acoustic/Seismic Signals of Unknown Structure]]>562101710296447<![CDATA[Ground Moving Target Refocusing in SAR Imagery Using Scaled GHAF]]>562103010454482<![CDATA[An Energy-Based Model Encoding Nonlocal Pairwise Pixel Interactions for Multisensor Change Detection]]>5621046105816317<![CDATA[Robust Interpolation of DEMs From Lidar-Derived Elevation Data]]>562105910689469<![CDATA[Prestack Seismic Inversion Based on Anisotropic Markov Random Field]]>562106910798504<![CDATA[Improving Land Surface Temperature and Emissivity Retrieval From the Chinese Gaofen-5 Satellite Using a Hybrid Algorithm]]>5621080109016316<![CDATA[The Mondrian Detection Algorithm for Sonar Imagery]]>5621091110211568<![CDATA[Remote Sensing Image Classification With Large-Scale Gaussian Processes]]>learns the optimal ones within a variational Bayes approach. The performance of the proposed methods is illustrated in complex problems of cloud detection from multispectral imagery and infrared sounding data. Excellent empirical results support the proposal in both computational cost and accuracy.]]>562110311143613<![CDATA[Results From the Deep Convective Clouds-Based Response Versus Scan-Angle Characterization for the MODIS Reflective Solar Bands]]>562111511285891<![CDATA[A Theoretical Framework for Change Detection Based on a Compound Multiclass Statistical Model of the Difference Image]]>562112911436973<![CDATA[Multilabel Remote Sensing Image Retrieval Using a Semisupervised Graph-Theoretic Method]]>562114411588364<![CDATA[An Algorithm for an Accurate Detection of Anomalies in Hyperspectral Images With a Low Computational Complexity]]>2, is proposed. It is based on two main processing stages. First, a set of characteristic pixels that best represent both anomaly and background classes are extracted applying orthogonal projection techniques. Second, the abundance maps associated to these pixels are estimated. Under the assumption that the anomaly class is composed of a scarce group of image pixels, rare targets can be identified from abundance maps characterized by a representation coefficient matrix with a large amount of almost zero elements. Unlike the other algorithms of the state of the art, the ADALOC^{2} algorithm has been specially designed for being efficiently implemented into parallel hardware devices for applications under real-time constraints. To achieve this, the ADALOC^{2} algorithm uses simple and highly parallelized operations, avoiding to perform complex matrix operations such as the computation of an inverse matrix or the extraction of eigenvalues and eigenvectors. An extensive set of simulations using the most representative state-of-the-art AD algorithms and both real and synthetic hyperspectral data sets have been conducted. Moreover, extra assessment metrics apart from classical receiver operating characteristic curves have been defined in order to make deeper comparisons. The obtained results clearly support the benefits of our proposal, both in terms of the accuracy of the detection results and the processing power demanded.]]>562115911766874<![CDATA[A Numerically Efficient Method for Predicting the Scattering Characteristics of a Complex Metallic Target Located Inside a Large Forested Area]]>562117711853280<![CDATA[High-Resolution RFI Localization Using Covariance Matrix Augmentation in Synthetic Aperture Interferometric Radiometry]]>562118611984331<![CDATA[Local Feature-Based Attribute Profiles for Optical Remote Sensing Image Classification]]>562119912123563<![CDATA[Corrections to “Segment-Oriented Depiction and Analysis for Hyperspectral Image Data” [Jul 17 3982-3996]]]>[1], information regarding the corresponding author is missing. The information is updated here. The updated footnote below shows that Xiaoyan Luo is the corresponding author for this paper.]]>5621213121321<![CDATA[Corrections to “Deep Recurrent Neural Networks for Hyperspectral Image Classification” [Jul 17 3639-3655]]]>[1]. The corrected OAs are underlined and shown in bold in Tables I–III.]]>56212141215537<![CDATA[IEEE Transactions on Geoscience and Remote Sensing information for authors]]>562C3C373<![CDATA[IEEE Transactions on Geoscience and Remote Sensing institutional listings]]>562C4C4429