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TOC Alert for Publication# 36 2014April 21<![CDATA[A Novel Rapid SAR Simulator Based on Equivalent Scatterers for Three-Dimensional Forest Canopies]]>529524352553041<![CDATA[Land Cover and Soil Type Mapping From Spaceborne PolSAR Data at L-Band With Probabilistic Neural Network]]>529525652703362<![CDATA[Regularized Simultaneous Forward–Backward Greedy Algorithm for Sparse Unmixing of Hyperspectral Data]]>0 problem directly and can exploit the joint sparsity among all the pixels in the hyperspectral data. In addition, the combination of the forward greedy step and the backward greedy step makes the RSFoBa more stable and less likely to be trapped into the local optimum than the conventional greedy algorithms. Furthermore, when updating the solution in each iteration, a regularizer that enforces the spatial-contextual coherence within the hyperspectral image is considered to make the algorithm more effective. We also show that the sublibrary obtained by the RSFoBa can serve as input for any other sparse unmixing algorithms to make them more accurate and time efficient. Experimental results on both synthetic and real data demonstrate the effectiveness of the proposed algorithm.]]>529527152883051<![CDATA[Narrow-Band Interference Mitigation for SAR Using Independent Subspace Analysis]]>529528953012216<![CDATA[Characterization of Marine Surface Slicks by Radarsat-2 Multipolarization Features]]>529530253194215<![CDATA[Comparison of Model Predictions With Measurements of Ku- and Ka-Band Near-Nadir Normalized Radar Cross Sections of the Sea Surface From the Genesis and Rapid Intensification Processes Experiment]]>° incidence angle, along with simultaneous wind truth from dropsonde observations, are compared with predictions of the “cutoff-invariant” two-scale model of sea scattering with the overall goal of assessing the model for possible future use in the APR-2 calibration process. The performance of the model as a function of wind speed and incidence angle is therefore emphasized. The measured data set, acquired primarily during the 2010 “Genesis and Rapid Intensification Processes” (GRIP) experiment, includes wind speeds from approximately 5 to 45 m/s. Model comparisons are limited by uncertainties in the wind fields due to limited dropsonde coverage; the data set is separated into “more reliable” (containing wind speeds of 5-20 m/s) and “less reliable” (wind speeds of 5-45 m/s) wind truth categories accordingly. Because a model of the sea spectrum is required for cutoff-invariant model predictions, comparisons with measured data are performed for three differing sea spectrum descriptions. It is found that a bias of less than ~ 1 dB over the wind speed range 5-40 m/s and a standard deviation less than 1 dB over the wind speed range 10-40 m/s can be achieved when using the “unified” sea spectrum description of Elfouhaily The model also provides error levels that are near uniform with respect to both incidence angle and wind speed.]]>529532053322530<![CDATA[A Novel Moving Target Imaging Algorithm for HRWS SAR Based on Local Maximum-Likelihood Minimum Entropy]]>529533353482461<![CDATA[MIMOSA: An Automatic Change Detection Method for SAR Time Series]]>529534953632490<![CDATA[Scattering Studies for Two-Dimensional Exponential Correlation Textured Rough Surfaces Using Small-Slope Approximation Method]]>529536453732294<![CDATA[Shadow Detection of Man-Made Buildings in High-Resolution Panchromatic Satellite Images]]>529537453861973<![CDATA[Thermal-Infrared Spectral and Angular Characterization of Crude Oil and Seawater Emissivities for Oil Slick Identification]]>52953875395825<![CDATA[Reduction of Signal-Dependent Noise From Hyperspectral Images for Target Detection]]>529539654111775<![CDATA[Temporal Decorrelation-Robust SAR Tomography]]>529541254211133<![CDATA[Fuzzy Content-Based Image Retrieval for Oceanic Remote Sensing]]>° - 21^{°}, N19^{°} - 45^{°}. This system conducts labeling and retrieval of the most relevant and typical mesoscale oceanic structures, such as upwellings, eddies, and island wakes located in the Canary Islands area and in the Mediterranean and Cantabrian seas. Our work is based on several soft computing technologies such as fuzzy logic and neurofuzzy systems.]]>52954225431838<![CDATA[A Self-Calibration Bundle Adjustment Method for Photogrammetric Processing of Chang <formula formulatype="inline"> <img src="/images/tex/21260.gif" alt="^{\prime }"> </formula>E-2 Stereo Lunar Imagery]]>'E-2 (CE-2) lunar orbiter is the second robotic orbiter in the Chinese Lunar Exploration Program. The charge-coupled-device (CCD) camera equipped on the CE-2 orbiter acquired stereo images with a resolution of less than 10 m and global coverage. High-precision topographic mapping with CE-2 CCD stereo imagery is of great importance for scientific research, as well as for the landing preparation and surface operation of the incoming Chang ^{'}E-3 lunar rover. Uncertainties in both the interior orientation (IO) model and exterior orientation (EO) parameters of the CE-2 CCD camera can affect mapping accuracy. In this paper, a self-calibration bundle adjustment method is proposed to eliminate these effects by adding several parameters into the IO model and fitting EO parameters using a third-order polynomial. The additional IO parameters and the EO polynomial coefficients are solved as unknowns along with ground points in the adjustment process. A series of strategies is adopted to ensure the robustness and reliability of the solution. Experimental results using images from two adjacent tracks indicated that this method effectively reduced the inconsistencies in the image space from approximately 20 pixels to subpixel. Topographic profiles generated using unadjusted and adjusted CE-2 data were compared with Lunar Orbiter Laser Altimeter data. These comparisons indicated that the local topographies generated after bundle adjustments, which reduced elevation differences by 9-10 m, were more consistent with LOLA data.]]>529543254421435<![CDATA[Large Earthquake Occurrence Estimation Based on Radial Basis Function Neural Networks]]>529544354531342<![CDATA[A Distributed Scatterer Interferometry Approach for Precision Monitoring of Known Surface Deformation Phenomena]]>529545454683832<![CDATA[GOST: A Geometric-Optical Model for Sloping Terrains]]>529546954821448<![CDATA[Validation of Near-Field Ground-Penetrating Radar Modeling Using Full-Wave Inversion for Soil Moisture Estimation]]>3/cm^{3} for the frequency domain and 0.016 cm^{3}/cm^{3} for the time-domain GPR. However, the accuracy reduced when the sand became wet. By performing numerical simulations, we found that it is due to the vertical heterogeneity of soil moisture under the effect of the hydrostatic pressure. We also showed that the GPR inversion with the multilayered soil model could account for this heterogeneity and improved the agreement between the modeled and measured GPR data as well as the accuracy of soil moisture estimation. As for the frequency dependence of the electrical properties, in the frequency ranges of both GPR systems, while the dielectric permittivity was approximately constant, the apparent conductivi-
y exponentially increased with increasing frequency. The success of the calibration and validation in laboratory conditions demonstrates a great potential for practical applications of the radar model, notably for the digital soil mapping and nondestructive testing of materials.]]>529548354971711<![CDATA[A Model of Surface Roughness for Use in Passive Remote Sensing of Bare Soil Moisture]]>52954985505840<![CDATA[A New Method of Tipping Calibration for Ground-Based Microwave Radiometer in Cloudy Atmosphere]]>529550655131291<![CDATA[An Improved Frequency Domain Focusing Method in Geosynchronous SAR]]>529551455282232<![CDATA[Automated Ice–Water Classification Using Dual Polarization SAR Satellite Imagery]]>529552955393497