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Geoscience and Remote Sensing, IEEE Transactions on

Issue 9 • Date Sept. 2014

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Displaying Results 1 - 25 of 69
  • Front Cover

    Page(s): C1
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  • IEEE Transactions on Geoscience and Remote Sensing publication information

    Page(s): C2
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  • Table of contents

    Page(s): 5241 - 6000
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  • A Novel Rapid SAR Simulator Based on Equivalent Scatterers for Three-Dimensional Forest Canopies

    Page(s): 5243 - 5255
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3036 KB) |  | HTML iconHTML  

    Synthetic aperture radar (SAR) simulation of 3-D forest canopies is a powerful tool for studying the interaction between radar and forest, for testing new applications, and for devising inversion algorithms of forest structures. SAR raw-signal generation is frequently used in point-target simulation but is rarely used in 3-D forest simulation. The existing simulators directly produce SAR images based on an impulse response function (IRF) without involving raw-signal generation and various nonideal factors. In this paper, a novel simulator to produce SAR images of 3-D forest canopies is proposed. It incorporates a SAR raw-signal generation process taking account of various nonideal factors such as trajectory deviation of radar platforms and complexity of natural environments, which is more faithful to realistic remote sensing systems. Furthermore, an approach to speed up the raw-signal generation is put forward based on the equivalent scattering model consisting of a few virtual scatterers with specially calculated positions and backscattering matrices. Thus, the raw signals received from the entire forest canopy can be equivalent to those from virtual scatterers in the case of tiny slant-range errors. The error sensitivity of equivalent conditions is analyzed, and the optimum selection of equivalent parameters is derived considering the compromise between precision and efficiency. The results of simulation and forest height inversion demonstrate the feasibility and potential utilities of the proposed simulator. View full abstract»

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  • Land Cover and Soil Type Mapping From Spaceborne PolSAR Data at L-Band With Probabilistic Neural Network

    Page(s): 5256 - 5270
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3357 KB) |  | HTML iconHTML  

    This paper evaluates performance of fully polarimetric SAR (PolSAR) data in several land cover mapping studies in the boreal forest environment, taking advantage of the high canopy penetration capability at L-band. The studies included multiclass land cover mapping, forest-nonforest delineation, and classification of soil type under vegetation. PolSAR data used in the study were collected by the ALOS PALSAR sensor in 2006-2007 over a managed boreal forest site in Finland. A supervised classification approach using selected polarimetric features in the framework of probabilistic neural network (PNN) was adopted in the study. It has no assumptions about statistics of the polarimetric features, using nonparametric estimation of probability distribution functions instead. The PNN-based method improved classification accuracy compared with standard maximum-likelihood approach. The improvement was considerably strong for soil type mapping under vegetation, indicating notable non-Gaussian effects in the PolSAR data even at L-band. The classification performance was strongly dependent on seasonal conditions. The PolSAR feature data set was further modified to include a number of recently proposed polarimetric parameters (surface scattering fraction and scattering diversity), reducing the computational complexity at practically no loss in the classification accuracy. The best obtained accuracies of up to 82.6% in five-class land cover mapping and more than 90% in forest-nonforest mapping in wall-to-wall validation indicate suitability of PolSAR data for wide-area land cover and forest mapping. View full abstract»

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  • Regularized Simultaneous Forward–Backward Greedy Algorithm for Sparse Unmixing of Hyperspectral Data

    Page(s): 5271 - 5288
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3041 KB) |  | HTML iconHTML  

    Sparse unmixing assumes that each observed signature of a hyperspectral image is a linear combination of only a few spectra (endmembers) in an available spectral library. It then estimates the fractional abundances of these endmembers in the scene. The sparse unmixing problem still remains a great difficulty due to the usually high correlation of the spectral library. Under such circumstances, this paper presents a novel algorithm termed as the regularized simultaneous forward-backward greedy algorithm (RSFoBa) for sparse unmixing of hyperspectral data. The RSFoBa has low computational complexity of getting an approximate solution for the l0 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. View full abstract»

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  • Narrow-Band Interference Mitigation for SAR Using Independent Subspace Analysis

    Page(s): 5289 - 5301
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    The mitigation of narrow-band interference (NBI) is an appealing topic in the synthetic aperture radar (SAR) community. It is an underdetermined single-channel separation problem. This paper proposes a method for NBI mitigation using the independent subspace analysis. First, each single pulse is transformed onto a manifold time-frequency distribution by the short-time Fourier transform (STFT). Then, the singular value analysis is carried out to extract the prominent features corresponding to the NBIs. Next, independent component analysis is employed to obtain statistically independent basis components. Furthermore, the independent subspaces corresponding to NBI are reconstructed and subtracted from the raw signal space. The signal with NBI mitigated is resynthesized by inverse STFT. Finally, after processing all the pulses, a well-focused SAR imagery is obtained by a conventional imaging algorithm. Experimental results of simulated and measured data have demonstrated the effectiveness of the proposed method. View full abstract»

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  • Characterization of Marine Surface Slicks by Radarsat-2 Multipolarization Features

    Page(s): 5302 - 5319
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4215 KB) |  | HTML iconHTML  

    In this paper, we study surface slick characterization in polarimetric C-band synthetic aperture radar (SAR) data. The objective is to identify the most powerful multipolarization SAR descriptors for mineral oil spill versus biogenic slick discrimination. A systematic comparison of eight well-known multipolarization features is provided. The analysis is performed on data that we collected during a large-scale oil spill exercise at the Frigg field situated northwest of Stavanger, in June 2011. Controlled oil spills and simulated look-alikes were simultaneously captured within fine quad-polarization Radarsat-2 acquisitions during this experiment. Multipolarization features derived from only the copolarized complex scattering coefficients are explored. We find that the two most powerful multipolarization features extracted from this data set are the geometric intensity, measuring the combined intensity based on the determinant of the coherency matrix, and the real part of the copolarization cross product, which is related to the scattering behavior of the target. We show that these two features can distinguish between the simulated biogenic slicks and mineral oil types such as Balder and Oseberg blend, and that the discriminative power seems to be persistent with time. View full abstract»

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  • 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

    Page(s): 5320 - 5332
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    A comparison of model predictions with measurements of near-nadir normalized radar cross sections (NRCSs) of the sea surface at Ku- and Ka-bands is reported. Measurements of Airborne Precipitation Radar Second Generation (APR-2) from near nadir to 25 ° 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. View full abstract»

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  • A Novel Moving Target Imaging Algorithm for HRWS SAR Based on Local Maximum-Likelihood Minimum Entropy

    Page(s): 5333 - 5348
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    For high-resolution wide-swath (HRWS) SAR based on multiple receive apertures in azimuth, this paper proposes a novel imaging approach for moving targets. This approach utilizes the wide bandwidth characteristics of the transmitted signal (multiple wavelengths) to estimate the moving target velocity. First, this paper explains that there is a phase mismatch (PM) between azimuth channels for the echo of a moving target, which depends on range frequency. In order to correct the PM, an algorithm based on local maximum-likelihood minimum entropy is proposed. The linear dependence of the PM on range frequency is employed to estimate the target velocity. Second, after the signal reconstruction in Doppler frequency and the compensation of the PM for a moving target, the estimated target velocity is utilized to implement the linear range cell migration correction and the Doppler centroid shifting. Then, the quadratic range cell migration is corrected by the keystone processing. After that, the focused moving target image can be obtained using the existing azimuth focusing approaches. Theoretical analysis shows that no interpolation is needed. The effectiveness of the imaging algorithm for moving targets is demonstrated via simulated and real measured ship HRWS ScanSAR data. View full abstract»

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  • MIMOSA: An Automatic Change Detection Method for SAR Time Series

    Page(s): 5349 - 5363
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2490 KB) |  | HTML iconHTML  

    This paper presents a new automatic change detection technique for synthetic aperture radar (SAR) time series, i.e., Method for generalIzed Means Ordered Series Analysis (MIMOSA). The method compares only two different temporal means between the amplitude images, whatever the length of the time series. The method involves three different steps: 1) estimation of the amplitude distribution parameters over the images; 2) computation of the theoretical joint probability density function between the two temporal means; and 3) automatic thresholding according to a given false alarm rate, which is the only change detection parameter. The procedure is executed with a very low computational cost and does not require any spatial speckle filtering. Indeed, the full image resolution is used. Due to the temporal means, the data volume to process is reduced, which is very helpful. Moreover, the two means can be simply updated using the new incoming images only. Thus, the full time series is not processed again. Change detection results between image pairs are presented with the airborne sensor CARABAS-II, using a public data release, and with TerraSAR-X data. In the case of time series, change detection results are illustrated using a TerraSAR-X time series. In every case, the MIMOSA method produces very good results. View full abstract»

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  • Scattering Studies for Two-Dimensional Exponential Correlation Textured Rough Surfaces Using Small-Slope Approximation Method

    Page(s): 5364 - 5373
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2294 KB) |  | HTML iconHTML  

    Two-dimensional exponential correlation rough surfaces characterized by textures are combined with the small-slope approximation (SSA) method to comparatively study electromagnetic (EM) scattering features of textured surfaces. The normalized copolarized radar cross section from 2-D exponential correlation rough surfaces characterized by stripe texture and block texture, respectively, is analyzed. Several numerical results show the effects of incident angle, texture angle, correlation length, and root-mean-square height on the copolarimetric scattering from the textured rough surface. The validity of the SSA method is verified by comparisons of theoretical value and measured data. Moreover, normalized amplitude distributions of backscattering fields from cells in a scene are studied through its statistical distribution and space correlation function, which are particularly useful for analysis and simulation of remote sensing images. Finally, based on the statistical distribution and space correlation function, the zero memory nonlinear transformation method is utilized to simulate EM scattering from very large scenes. The simulated scene coincides with the original one quite well. View full abstract»

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  • Shadow Detection of Man-Made Buildings in High-Resolution Panchromatic Satellite Images

    Page(s): 5374 - 5386
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1969 KB) |  | HTML iconHTML  

    High-resolution satellite imagery is considered an excellent candidate for extracting information about the human activities on Earth. The information about residential development and suburban area mapping is of interest that can be obtained from these images. Shadow of structures such as man-made buildings is one of the main cues for structure detection in panchromatic high-resolution satellite imagery. However, to correctly exploit the information of the shadow in an image, the shadow needs to be detected and isolated first. In this paper, we propose a new algorithm for shadow detection and isolation of buildings in high-resolution panchromatic satellite imagery. The proposed algorithm is based on tailoring the traditional model of the geometric active contours such that the new model of the contours is systematically biased toward segmenting the shadow and the dark regions in the image. The systematic biasing in the proposed contour model is accomplished by novel encoding of the radiometric characteristics of the shadows regions. After detecting and segmenting the shadow and the dark regions in the image, further processing steps are introduced. The proposed postprocessing is based on selection of optimal threshold and a boundary complexity metric to distinguish the true shadows from the clutter. Experimental results are presented to validate the performance of the proposed algorithm on real high-resolution panchromatic satellite images. View full abstract»

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  • Thermal-Infrared Spectral and Angular Characterization of Crude Oil and Seawater Emissivities for Oil Slick Identification

    Page(s): 5387 - 5395
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (821 KB) |  | HTML iconHTML  

    Previous work has shown that the emissivity of crude oil is lower than that of the seawater in the thermal-infrared (TIR) spectrum. Thus, oil slicks cause an emissivity decrease relative to the seawater in that region. The aim of this paper was to carry out experimental measurements to characterize the spectral and angular variations of crude oil and seawater emissivities. The results showed that the crude oil emissivity is lower than the seawater emissivity and that it is essentially flat in the atmospheric window of 8-13 μm. The crude oil emissivity has a marked emissivity decrease with the angle (from 0.956 ± 0.005 at 15 ° to 0.873 ± 0.007 at 65 °), which is even higher than that of the seawater, and thus, the seawater-crude emissivity difference increases with the angle (from +0.030 ±0.007 at close-to-nadir angles up to +0.068 ±0.010 in average at 65 °). In addition, the experimental results were checked by using the dual-angle viewing capability of the Environmental Satellite Advanced Along-Track Scanning Radiometer (ENVISAT-AATSR) images (i.e., 0 °-22 ° and 53 °-55 ° for the nadir and forward views, respectively), with the data acquired during the BP Deepwater Horizon oil slick in 2010. The objective was to explore its applicability to satellite observations. Nadir-forward emissivity differences of +0.028 and +0.017 were obtained for the oil slick and the surrounding clean seawater, respectively. The emissivity differences between the seawater and the oil slick were +0.035 and +0.046 for the nadir and forward views, respectively, which was in agreement with the experimental data. The increase in the seawater-crude emissivity difference with the angle gives significant differences for off-nadir observation angles, showing a new chance of crude oil slick identification from satellite TIR data. View full abstract»

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  • Reduction of Signal-Dependent Noise From Hyperspectral Images for Target Detection

    Page(s): 5396 - 5411
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1765 KB) |  | HTML iconHTML  

    Tensor-decomposition-based methods for reducing random noise components in hyperspectral images (HSIs), both dependent and independent from signal, are proposed. In this paper, noise is described by a parametric model that accounts for the dependence of noise variance on the signal. This model is thus suitable for the cases where photon noise is dominant compared with the electronic noise contribution. To denoise HSIs distorted by both signal-dependent (SD) and signal-independent (SI) noise, some hybrid methods, which reduce noise by two steps according to the different statistical properties of those two types of noise, are proposed in this paper. The first one, named as the PARAFACSI- PARAFACSD method, uses a multilinear algebra model, i.e., parallel factor analysis (PARAFAC) decomposition, twice to remove SI and SD noise, respectively. The second one is a combination of the well-known multiple-linear-regression-based approach termed as the HYperspectral Noise Estimation (HYNE) method and PARAFAC decomposition, which is named as the HYNE-PARAFAC method. The last one combines the multidimensional Wiener filter (MWF) method and PARAFAC decomposition and is named as the MWF-PARAFAC method. For HSIs distorted by both SD and SI noise, first, most of the SI noise is removed from the original image by PARAFAC decomposition, the HYNE method, or the MWF method based on the statistical property of SI noise; then, the residual SD components can be further reduced by PARAFAC decomposition due to its own statistical property. The performances of the proposed methods are assessed on simulated HSIs. The results on the real-world airborne HSI Hyperspectral Digital Imagery Collection Experiment (HYDICE) are also presented and analyzed. These experiments show that it is worth taking into account noise signal-dependence hypothesis for processing HYDICE data. View full abstract»

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  • Temporal Decorrelation-Robust SAR Tomography

    Page(s): 5412 - 5421
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1129 KB) |  | HTML iconHTML  

    Much interest is continuing to grow in advanced interferometric synthetic aperture radar (SAR) methods for full 3-D imaging, particularly of volumetric forest scatterers. Multibaseline (MB) SAR tomographic elevation beam forming, i.e., spatial spectral estimation, is a promising technique in this framework. In this paper, the important effect of temporal decorrelation during the repeat-pass MB acquisition is tackled, analyzing the impact on superresolution (MUSIC) tomography with limited sparse data. Moreover, new tomographic methods robust to temporal decorrelation phenomena are proposed, exploiting the advanced differential tomography concept that produces “space-time” signatures of scattering dynamics in the SAR cell. To this aim, a 2-D version of MUSIC and a generalized MUSIC method matched to nonline spectra are applied to decouple the nuisance temporal signal components in the spatial spectral estimation. Simulated analyses are reported for different geometrical and temporal parameters, showing that the new concept of restoring tomographic performance in temporal decorrelating forest scenarios through differential tomography is promising. View full abstract»

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  • Fuzzy Content-Based Image Retrieval for Oceanic Remote Sensing

    Page(s): 5422 - 5431
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (838 KB) |  | HTML iconHTML  

    The detection of mesoscale oceanic structures, such as upwellings or eddies, from satellite images has significance for marine environmental studies, coastal resource management, and ocean dynamics studies. Nevertheless, there is a lack of tools that allow us to retrieve automatically relevant mesoscale structures from large satellite image databases. This paper focuses on the development and validation of a content-based image retrieval system to classify and retrieve oceanic structures from satellite images. The images were obtained from the National Oceanic and Atmospheric Administration satellite's Advanced Very High Resolution Radiometer sensor. The study area is about W2° - 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. View full abstract»

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  • A Self-Calibration Bundle Adjustment Method for Photogrammetric Processing of Chang $^{prime}$E-2 Stereo Lunar Imagery

    Article#: 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1429 KB) |  | HTML iconHTML  

    Chang '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. View full abstract»

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  • Large Earthquake Occurrence Estimation Based on Radial Basis Function Neural Networks

    Page(s): 5443 - 5453
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1337 KB) |  | HTML iconHTML  

    This paper presents a novel scheme for the estimation of large earthquake event occurrence based on radial basis function (RBF) neural network (NN) models. The input vector to the network is composed of different seismicity rates between main events, which are easy to calculate in a reliable manner. Training of the NNs is performed using the powerful fuzzy means training algorithm, which, in this case, is modified to incorporate a leave-one-out training procedure. This helps the algorithm to account for the limited number of training data, which is a common problem when trying to model earthquakes with data-driven techniques. Additionally, the proposed training algorithm is combined with the Reasenberg clustering technique, which is used to remove aftershock events from the catalog prior to processing the data with the NN. In order to evaluate the performance of the resulting framework, the method is applied on the California earthquake catalog. The results show that the produced RBF model can successfully estimate interevent times between significant seismic events, thus resulting to a predictive tool for earthquake occurrence. A comparison with a different NN architecture, namely, multilayer perceptron networks, highlights the superiority of the proposed approach. View full abstract»

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  • A Distributed Scatterer Interferometry Approach for Precision Monitoring of Known Surface Deformation Phenomena

    Page(s): 5454 - 5468
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3832 KB) |  | HTML iconHTML  

    This paper presents a new technique for mapping mean deformation velocity in highly decorrelated areas with known deformation patterns, exploiting high-resolution synthetic aperture radar (SAR) data. The implemented method is based on distributed scatterers and first makes use of the Anderson-Darling (AD) statistical test to identify homogenous patches of pixels based on SAR amplitude images. Then, a robust object adaptive parameter estimation is performed to estimate the local gradients of deformation velocity and the local gradients of residual DEM in range and azimuth directions for these patches, utilizing small baseline differential interferograms. Finally, the information obtained from different patches is connected to get the deformation velocity, via a 2-D model-based deformation integration using Bayesian inference. Compared with published multitemporal interferometric work, the main advantage of the newly developed algorithm is that it does not require any phase unwrapping, and because of this, the method is largely insensitive to decorrelation phenomenon occurring in natural terrains and the availability of persistent scatterers (PSs), in contrast to the coherent stacking techniques such as PS interferometry, small baseline subset algorithm, and SqueeSAR. The method is computationally inexpensive with respect to SqueeSAR as only the small baseline interferograms are used for the processing. The method provides spatially dense deformation velocity maps at a suitable object resolution, as compared with a few measured points provided by the stacking techniques in difficult decorrelated regions. High Resolution Spotlight TerraSAR-X data set of Lueneburg in Germany is used as a processing example of this technique. View full abstract»

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  • GOST: A Geometric-Optical Model for Sloping Terrains

    Page(s): 5469 - 5482
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1442 KB) |  | HTML iconHTML  

    GOST is a geometric-optical (GO) model for sloping terrains developed in this study based on the four-scale GO model, which simulates the bidirectional reflectance distribution function (BRDF) of forest canopies on flat surfaces. The four-scale GO model considers four scales of canopy architecture: tree groups, tree crowns, branches, and shoots. In order to make this model suitable for sloping terrains, the mathematical description for the projection of tree crowns on the ground has been modified to consider the fact that trees grow vertically rather than perpendicularly to sloping grounds. The simulated canopy gap fraction and the area ratios of the four scene components (sunlit foliage, sunlit background, shaded foliage, and shaded background) by GOST compare well with those simulated by 3-D virtual canopy computer modeling techniques for a hypothetical forest. GOST simulations show that the differences in area ratios of the four scene components between flat and sloping terrains can reach up to 50%-60% in the principal plane and about 30% in the perpendicular plane. Two case studies are conducted to compare modeled canopy reflectance with observations. One comparison is made against Landsat-5 Thematic Mapper (TM) reflectance, demonstrating the ability of GOST to model canopy reflectance variations with slope and aspect of the terrain. Another comparison is made against MODIS surface reflectance, showing that GOST with topographic consideration outperforms that without topographic consideration. These comparisons confirm the ability of GOST to model canopy reflectance on sloping terrains over a large range of view angles. View full abstract»

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  • Validation of Near-Field Ground-Penetrating Radar Modeling Using Full-Wave Inversion for Soil Moisture Estimation

    Page(s): 5483 - 5497
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1711 KB) |  | HTML iconHTML  

    We present validation results of a new ground-penetrating radar (GPR) near-field model for determining the electrical properties and correlated water content of a sand using both frequency- and time-domain radars. The radar antennas are intrinsically characterized using an equivalent set of infinitesimal source/field points and characteristic functions of antennas, which were determined using measurements with the antenna at different distances from a copper plane. The antenna radiation was modeled using six source and field points, which was found to be a good compromise between high modeling accuracy and computing efficiency. We validated our model by inverting GPR data to predict the water content of a sand layer subject to seven levels of saturation. A soil dielectric mixing model was integrated into the full-wave GPR inverse modeling to directly estimate the water content and to account for the frequency dependence of the electrical properties. Although the quality of the fit slightly decreased as the antenna approached the sand surface, the results showed a close agreement between measured and modeled data, resulting in accurate estimation of the water content. The average errors of all water content estimates were 0.012 cm3/cm3 for the frequency domain and 0.016 cm3/cm3 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. View full abstract»

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  • A Model of Surface Roughness for Use in Passive Remote Sensing of Bare Soil Moisture

    Article#: 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (840 KB) |  | HTML iconHTML  

    Spaceborne radiometers operating near 1.4 GHz are the primary instrument for recent efforts to remotely sense nearsurface soil moisture around the globe. Generally, these instruments must contend with the effects of vegetation growing in the soil. However, an important first step is to model the measurements made by a radiometer that is viewing bare (vegetation-free) soil. The proposed model uses a matching layer and a random depolarizer to describe bare soil surface roughness and some aspects of antenna beamwidth. The model suggests that the effects of nearsurface soil moisture and roughness upon the radiometer measurement are more distinct than is currently thought. Furthermore, it appears that both moisture and roughness can be retrieved from a single set of radiometer measurements made at orthogonal linear polarizations. This retrieval precision is predicted to be poor at soil observation angles near nadir but improves for larger angles. At observation angles near 50°, the vertically polarized radiometer measurements are predicted to be nearly insensitive to roughness. A convenient parameterization of the model is provided and permits quick implementation. View full abstract»

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  • A New Method of Tipping Calibration for Ground-Based Microwave Radiometer in Cloudy Atmosphere

    Page(s): 5506 - 5513
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1287 KB) |  | HTML iconHTML  

    Tipping calibration is an important technique for the absolute calibration of ground-based microwave radiometers. The standard tipping calibration is usually used in clear atmosphere. The spatial variations of liquid water in cloudy atmosphere may lead to great calibration errors in the process of standard tipping calibration. A new method of tipping calibration for ground-based microwave radiometers is proposed, which uses an accurate channel to calibrate another inaccurate channel in cloudy atmosphere. The simulation and measurements show that the method proposed here can eliminate the radiation of cloud liquid water and calibrate the radiometer under cloudy-sky conditions effectively. A relative tipping calibration is also introduced to retrieve the parameters in cloudy atmosphere. It has been proven that the relative tipping calibration can reduce the retrieval error significantly in cloudy atmosphere even though neither of the two channels is accurate. View full abstract»

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  • An Improved Frequency Domain Focusing Method in Geosynchronous SAR

    Page(s): 5514 - 5528
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    Geosynchronous (GEO) SAR has been proposed as a means for obtaining observations of the Earth with finer temporal sampling than possible with a single satellite from a lower orbit. However, standard algorithms developed for low-Earth-orbit SAR imaging are inadequate for GEO, where the typical assumptions of quasi-linear trajectory and “stop-and-go” transmit/receive propagation break down because of the long integration time and the very long range between the satellite and the Earth. This paper proposes a curved trajectory model to overcome these limitations and considers the impact of the “stop-and-go” assumption. According to the proposed range model, an accurate 2-D analytical spectrum is deduced under the curved trajectory model based on a series reversion method, leading to an improved frequency domain imaging algorithm involving a high-order-phase coupling function and a range migration correction function. An adaptive azimuth compression function overcomes the space variance for large-scene focusing. Simulation results validate that the improved imaging algorithm performs well over the expected range of applicability for GEO SAR. View full abstract»

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Aims & Scope

 

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (TGRS) is a monthly publication that focuses on the theory, concepts, and techniques of science and engineering as applied to sensing the land, oceans, atmosphere, and space; and the processing, interpretation, and dissemination of this information.

 

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Meet Our Editors

Editor-in-Chief
Antonio J. Plaza
University of Extremadura