<![CDATA[ IEEE Geoscience and Remote Sensing Letters - new TOC ]]>
http://ieeexplore.ieee.org
TOC Alert for Publication# 8859 2016June 23<![CDATA[Front Cover]]>137C1C1363<![CDATA[IEEE Geoscience and Remote Sensing Letters publication information]]>137C2C246<![CDATA[Table of Contents]]>137885886130<![CDATA[Two-Dimensional Spectrum for Circular Trace Scanning SAR Based on an Implicit Function]]>137887891751<![CDATA[Pan-Sharpening by Multilevel Interband Structure Modeling]]>1378928961319<![CDATA[Tensor Decomposition and PCA Jointed Algorithm for Hyperspectral Image Denoising]]>1378979011053<![CDATA[Adaptive Laplacian Eigenmap-Based Dimension Reduction for Ocean Target Discrimination]]>137902906925<![CDATA[Transient Interference Mitigation via Supervised Matrix Completion]]>137907911478<![CDATA[Higher Order Statistics for Texture Analysis and Physical Interpretation of Polarimetric SAR Data]]>137912916893<![CDATA[Uncertainty Estimation of Local-Scale Land Surface Temperature Products Over Urban Areas Using Monte Carlo Simulations]]>1379179211100<![CDATA[Band Weighting via Maximizing Interclass Distance for Hyperspectral Image Classification]]>137922925947<![CDATA[Quantification of Water Ice in the Hermite-A Crater of the Lunar North Pole]]>137926930864<![CDATA[Unsupervised SAR Image Change Detection Based on SIFT Keypoints and Region Information]]>1379319351664<![CDATA[Reflective Imaging Solved by the Radon Transform]]>137936938377<![CDATA[An Algorithm to Extract More Accurate Slopes From DEMs]]>$times$ 2 cell neighborhood model instead of the 3 $times$ 3 one so that the center cell's own elevation becomes the most important factor during calculation. While the horizontal and vertical cells are twice as important as the corn cell, the center cell gets twice larger than the corn one. The comparison between Horn and Quad is based on three different artificial surfaces, in which we can obtain the true slope value by derivation. Experiments indicate that Quad calculates the slope more accurately, and from the results of Student's $t$-test, its performance is significantly better than Horn.]]>137939942880<![CDATA[Refraction Angle Approximation Algorithm for Wall Compensation in TWRI]]>1379439461008<![CDATA[A Novel Noise Filtering Model for Photon-Counting Laser Altimeter Data]]>1379479511249<![CDATA[GBM-Based Unmixing of Hyperspectral Data Using Bound Projected Optimal Gradient Method]]>$O(1/k^{2})$, with $k$ denoting the number of iterations in BPOGM. We further apply the BPOGM to solve the GBM-based unmixing problem. Experiments on both synthetic data sets and real hyperspectral images demonstrate that the BPOGM is efficient for solving the GBM-based unmixing problem.]]>1379529561036<![CDATA[Full-Wave Scattering and Imaging Characterization of Realistic Trees for FOPEN Sensing]]>1379579611790<![CDATA[An Efficient Infrared Small Target Detection Method Based on Visual Contrast Mechanism]]>137962966645<![CDATA[One-Class Gaussian Process for Possibilistic Classification Using Imaging Spectroscopy]]>137967971569<![CDATA[High-Resolution Soil Moisture Retrieval With ASCAT]]>$(sigma^{0})$ of the Earth can be used to estimate soil moisture levels over land. Such estimates are currently produced at 25- and 50-km resolution using the Advanced Scatterometer (ASCAT) sensor and a change detection algorithm originally developed at the Vienna University of Technology (TU-Wien). Using the ASCAT spatial response function (SRF), high-resolution (approximately 15–20 km per pixel) images of $sigma^{0}$ can be produced, enabling the creation of a high-resolution soil moisture product using a modified version of the TU-Wien algorithm. The high-resolution soil moisture images are compared to images produced with the Water Retrieval Package 5.5 algorithm, which is also based on the TU-Wien algorithm, and to in situ measurements from the National Oceanic and Atmospheric Administration U.S. Climate Reference Network (NOAA CRN). The WARP 5.5 and high-resolution image products generally show good agreement with each other; the high-resolution estimates appear to resolve soil moisture features at a finer scale and demonstrate a tendency toward greater moisture values in some areas. When compared to volumetric soil moisture measurements from NOAA CRN stations for 2010 and 2011, the WARP 5.5 and high-resolution soil moisture estimates perform similarly, with both having a root-mean-square difference from the in situ data of approximately 0.06 m^{3}/m^{3} in one study area and 0.09 m^{3}/m^{3} in another.]]>137972976812<![CDATA[Excitation Wavelength Analysis of Laser-Induced Fluorescence LiDAR for Identifying Plant Species]]>137977981645<![CDATA[Improved SNR Optimum Method in POLDINSAR Coherence Optimization]]>$+$ 2” optimization problem). However, SNR-OPT still costs much time. In this letter, we propose a new method which can further make this “2 $+$ 2” optimization problem transform into one 2-D and two independent 1-D optimization problems (“ $2+1+1$” optimization problem). Thus, the computational efficiency will be improved much more compared with SNR-OPT, and meanwhile, the accuracy just decreases a little. Seven full polarimetric RADARSAT-2 images are taken for experiment, and the results also show that the improved SNR-OPT-CG-CGM method is a better method considering the tradeoff between computation time and accuracy compared with other methods for DINSAR applications.]]>137982986682<![CDATA[Region-Based Retrieval of Remote Sensing Images Using an Unsupervised Graph-Theoretic Approach]]>137987991559<![CDATA[An Indoor Backpack System for 2-D and 3-D Mapping of Building Interiors]]>$x$, $y$, and $z$ positions and roll, yaw, and pitch angles). First, we present a 6-DOF pose estimation algorithm by fusing 2-D laser scanner data with inertial sensor data using an extended Kalman filter-based method. The estimated 6-DOF pose is used as the initialized transformation for consecutive map alignment in 3-D map building. The 6-DOF pose gives a full 3-D estimation of the system pose and is used to accelerate the map alignment process and also align the two maps directly when there are few or no overlapping areas between the maps. Our results show that the proposed system effectively builds a consistent 2-D grid map and a 3-D point cloud map of an indoor environment.]]>1379929961213<![CDATA[Cross-Correlation Between Polarization Channels in SAR Imagery Over Oceanographic Features]]>13799710011457<![CDATA[Hopfield Neural Network Approach for Supervised Nonlinear Spectral Unmixing]]>13710021006687<![CDATA[Multimetric Active Learning for Classification of Remote Sensing Data]]>$k$- nearest neighbor classification to enrich the set of labeled samples. Experiments on two sets of remote sensing data illustrate the effectiveness of the proposed framework in terms of both classification accuracy and computational requirements.]]>137100710111140<![CDATA[Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval]]>13710121016529<![CDATA[Computationally Efficient Transient Interference Excision Method for Skywave Over-the-Horizon Radar]]>13710171021439<![CDATA[Improved Channel Error Calibration Algorithm for Azimuth Multichannel SAR Systems]]>137102210261122<![CDATA[Technical Aspects of 205 MHz VHF Mini Wind Profiler Radar for Tropospheric Probing]]>13710271031747<![CDATA[Sparsity-Driven Change Detection in Multitemporal SAR Images]]>$ell_{1}$-norm-based total variation (TV) regularization term. Log-ratio terms model the changes between the two SAR images where the TV regularization term imposes smoothness on these changes in a sparse manner such that fine details are extracted while effects like speckle noise are reduced. The proposed method, sparsity-driven change detection (SDCD), employs accurate approximation techniques for the minimization of the cost function since data fidelity terms are not convex and the employed $ell_{1}$-norm TV regularization term is not differentiable. The performance of the SDCD is shown on real-world SAR images obtained from various SAR sensors.]]>137103210361341<![CDATA[IEEE Geoscience and Remote Sensing Letters information for authors]]>137C3C333<![CDATA[IEEE Geoscience and Remote Sensing Letters Institutional Listings]]>137C4C4120