<![CDATA[ Geoscience and Remote Sensing Letters, IEEE - new TOC ]]>
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TOC Alert for Publication# 8859 2014July 10<![CDATA[Out-of-Band Ambiguity Analysis of Nonuniformly Sampled SAR Signals]]>111220272031648<![CDATA[Assessment of Similarity Between Well Logs Using Synchronization Measures]]>111220322036541<![CDATA[An Efficient Approach for VIIRS RDR to SDR Data Processing]]>111220372041208<![CDATA[Time-Frequency Analysis of Seismic Data Using Synchrosqueezing Transform]]>111220422044767<![CDATA[Robust Image Registration Using Structure Features]]>111220452049567<![CDATA[On the Baseband Doppler Centroid Estimation for Multichannel HRWS SAR Imaging]]>111220502054697<![CDATA[Superresolution Mapping Using Multiple Dictionaries by Sparse Representation]]>111220552059944<![CDATA[Equation-Based <roman>InSAR</roman> Data Quadtree Downsampling for Earthquake Slip Distribution Inversion]]>111220602064908<![CDATA[Maps of PWV Temporal Changes by SAR Interferometry: A Study on the Properties of Atmosphere's Temperature Profiles]]>$(Deltahbox{PWV})$ from the propagation delay of radar signal in atmosphere. The relationship between $Deltahbox{PWV}$ and propagation delay mainly depends on the vertical profiles of temperature and water vapor pressure. In this letter, we present a methodology to study the spatial and temporal variations of the temperature's vertical profile and generate more accurate high-resolution $Deltahbox{PWV}$ maps by means of InSAR.]]>111220652069366<![CDATA[Automatic Detection of Inshore Ships in High-Resolution Remote Sensing Images Using Robust Invariant Generalized Hough Transform]]>111220702074196<![CDATA[Automatic GCP Extraction in Mountainous Areas Using DEM and PolSAR Data]]>1112207520791098<![CDATA[Digital Elevation Model Reconstruction in Multichannel Spaceborne/Stationary SAR Interferometry]]>a posteriori (MAP). Spaceborne/stationary SAR is a typical bistatic SAR configuration. In order to solve the phase disconnection problem while working with a low signal-to-noise ratio and a limited number of baselines as well as having large look angle variations, we use the height estimation results derived from iterative multibaseline unwrapping as the initial heights for the MAP estimation. The method presented here is highly experimental. An experiment is carried out to verify the effectiveness of the proposed approach. In this experiment, TerraSAR-X, working in the high-resolution spotlight mode with a 300-MHz bandwidth, acts as the transmitter. The receivers with three echo channels are placed on the ground to receive the reflected waveform. As proof of concept, we demonstrate the height estimation of several buildings.]]>1112208020842037<![CDATA[Sea Ice Concentration Retrieval Using Composite ScanSAR Features in a SAR Data Assimilation Process]]>111220852089649<![CDATA[Improving the Accuracy of Soil Moisture Retrievals Using the Phase Difference of the Dual-Polarization GNSS-R Interference Patterns]]>1112209020941029<![CDATA[Polarimetric Target Decomposition Based on Attributed Scattering Center Model for Synthetic Aperture Radar Targets]]>111220952099915<![CDATA[Spatial Resolution Enhancement of Hyperspectral Images Using Unmixing and Binary Particle Swarm Optimization]]>111221002104779<![CDATA[A Subspace-Based Multinomial Logistic Regression for Hyperspectral Image Classification]]>111221052109700<![CDATA[An Algorithm for Sea-Surface Wind Field Retrieval From GNSS-R Delay-Doppler Map]]>ad hoc correction is made on the simulated antenna pattern. The retrieved wind results are compared with the in situ measurements provided by the National Data Buoy Center. The results show that an error of 1 m/s in the wind speed and 30 $^{circ}$ in the wind direction can be obtained with a lower threshold set as 30% to 42% of the peak DDM point.]]>111221102114739<![CDATA[Stream Model-Based Orthorectification in a GPU Cluster Environment]]>111221152119431<![CDATA[Sample Discriminant Analysis for SAR ATR]]>111221202124412<![CDATA[A Geostatistical Approach to Upscale Soil Moisture With Unequal Precision Observations]]>111221252129784<![CDATA[Bayesian Framework to Wavelet Estimation and Linearized Acoustic Inversion]]>a posteriori impedance can be calculated, yielding a very fast inversion algorithm. Results of tests on real data are compared with the deterministic constrained sparse-spike inversion, indicating that our proposal is viable and reliable.]]>1112213021341180<![CDATA[SparseCEM and SparseACE for Hyperspectral Image Target Detection]]>$ell_{1}$-norm regularization term to restrict the output to be sparse. Furthermore, we convert our detection models to second-order cone program problems, which can be efficiently solved by using the interior point method. The experiments on two real hyperspectral images demonstrate the effectiveness of the proposed algorithms.]]>111221352139525<![CDATA[Edge Extraction for Polarimetric SAR Images Using Degenerate Filter With Weighted Maximum Likelihood Estimation]]>111221402144942<![CDATA[A Matching Method for Establishing Correspondence Between Satellite Radar Altimeter Data and Transponder Data Generated During Calibration]]>in situ calibration. The transponder generates a measurement error when it measures the arrival time of the altimeter's transmitted signal and embeds the error in both the transponder's recorded data and the altimeter's recorded data. The second-order finite difference sequence of this error sequence can be extracted from the raw data; thus, the correspondence between two identical but mismatched second-order difference sequences can be uniquely established. The measurement error is utilized, and a data matching method that can uniquely establish the correspondence between the altimeter's recorded data sequence and the transponder's recorded data sequence is presented. This postprocessing method does not increase the real-time signal processing workload of the transponder. Furthermore, the principles underlying this method can be used for any transponder that can adjust the response signal delay during calibration.]]>111221452149765<![CDATA[A Learning-Based Resegmentation Method for Extraction of Buildings in Satellite Images]]>111221502153482<![CDATA[Two-Antenna SAR With Waveform Diversity for Ground Moving Target Indication]]>111221542158828<![CDATA[Optimal Detector Synthesis for a Spaceborne High-Precision Oceanographic Radar Altimeter]]>111221592162179<![CDATA[Ionospheric Polarimetric Dispersion Effect on Low-Frequency Spaceborne SAR Imaging]]>111221632167558<![CDATA[A Robust Infrared Small Target Detection Algorithm Based on Human Visual System]]>111221682172681<![CDATA[Digital Beamforming on Receive in Elevation for Multidimensional Waveform Encoding SAR Sensing]]>a posteriori digital beamforming (DBF) in elevation for multidimensional waveform encoding SAR sensing. The signal-to-noise ratio scaling factor in elevation and full-Doppler-band range ambiguity-to-signal ratio are defined to give global assessment of the DBF approach. Constraints for the selection of elevation subaperture number and their relevant influence on image quality are investigated. By theoretical analysis and simulation, the subaperture number can be appropriately selected by a tradeoff between the data volume and imaging performance. Further strategies for data reduction and performance optimization are briefly discussed.]]>111221732177483<![CDATA[Similarities Between Spaceborne Active and Airborne Passive Microwave Observations at 1 km Resolution]]>$({rm r}^{2}=0.37)$ suggest that very high-resolution C-band radar data may be used to describe subpixel heterogeneity within coarse resolution radiometer data, such as the future Soil Moisture Active Passive mission.]]>111221782182851<![CDATA[Three-Dimensional Wavelet Texture Feature Extraction and Classification for Multi/Hyperspectral Imagery]]>1112218321871437<![CDATA[Cross-Polarization Radar Backscattering From the Ocean Surface and Its Dependence on Wind Velocity]]>$^{circ}$–50 $^{circ}$), the magnitude of these angular variations is equivalent to a difference of about 10 m/s in the retrieved wind speed for low-to-strong winds ( $<,sim$21 m/s) and probably for strong-to-severe winds ( $>,sim$21 m/s) as well. It is prudent to incorporate the incidence angle dependence and the azimuth angle dependence in the wind retrieval algorithm and in the signal simulation for the design of next-generation scatterometers. The dependence on the wind speed is also examined. It reconfirms that the VH sensitivity increases toward high winds, but signal saturation may occur.]]>111221882192791<![CDATA[Pattern Matching for Building Feature Extraction]]>111221932197376<![CDATA[An Efficient Algorithm for Depression Filling and Flat-Surface Processing in Raster DEMs]]>$O(Mlog_{2}M)$ time complexity, where $M$ is less than the total number of cells, which is more efficient than the algorithm proposed by Wang and Liu. In addition, the improved algorithm not only fills depressions but also elevates flat surfaces for the convenience of extracting flow directions. Furthermore, to adapt to different data types, for example, integer, single-precision floating point, and double precision, the improved algorithm does not alter flat-surface elevations in DEMs directly but uses a mask matrix to mark the incremental elevation values of flat surfaces. In speed comparison testing, the improved algorithm performed up to 16%–32% faster than the original.]]>111221982202987