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Geoscience and Remote Sensing, IEEE Transactions on

Issue 5 • Date May 2014

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Displaying Results 1 - 25 of 66
  • Front cover

    Publication Year: 2014 , Page(s): C1
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  • IEEE Transactions on Geoscience and Remote Sensing publication information

    Publication Year: 2014 , Page(s): C2
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  • Table of contents

    Publication Year: 2014 , Page(s): 2293 - 3036
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  • Full-Wave Modeling of Near-Field Radar Data for Planar Layered Media Reconstruction

    Publication Year: 2014 , Page(s): 2295 - 2303
    Cited by:  Papers (13)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1199 KB) |  | HTML iconHTML  

    A new near-field radar modeling approach for wave propagation in planar layered media is presented. The radar antennas are intrinsically modeled using an equivalent set of infinitesimal electric dipoles and characteristic, frequency-dependent, global reflection, and transmission coefficients. These coefficients determine through a plane wave decomposition wave propagation between the radar reference plane, point sources, and field points. The interactions between the antenna and layered medium are thereby inherently accounted for. The fields are calculated using 3-D Green's functions. We validated the model using an ultrawideband frequency-domain radar with a transmitting and receiving Vivaldi antenna operating in the range 0.8-4 GHz. The antenna characteristic coefficients are obtained from near- and far-field measurements over a copper plane. The proposed model provides unprecedented accuracy for describing near-field radar measurements collected over a water layer, the frequency-dependent electrical properties of which were described using the Debye model. Layer thicknesses could be retrieved through full-wave inversion. The proposed approach demonstrated great promise for nondestructive testing of planar materials and digital soil mapping using ground-penetrating radar. View full abstract»

    Open Access
  • Spectral-Based Estimation of the Local Azimuth Ambiguity-to-Signal Ratio in SAR Images

    Publication Year: 2014 , Page(s): 2304 - 2313
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1270 KB) |  | HTML iconHTML  

    An innovative technique to estimate the local azimuth ambiguity-to-signal ratio (AASR) in synthetic aperture radar (SAR) images is presented. Unlike the backscatter-based (BB) technique, the proposed one, which is based on the spectral properties of the image, does not require that the areas responsible for the ambiguity lie within the focused image. Analysis of real TerraSAR-X data shows that the estimates of the proposed technique are consistent with the BB ones. Moreover, according to simulations, the proposed technique seems to provide more accurate estimates than the BB method, especially for high values of the local AASR. View full abstract»

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  • Hyperspectral Image Denoising With a Spatial–Spectral View Fusion Strategy

    Publication Year: 2014 , Page(s): 2314 - 2325
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1924 KB) |  | HTML iconHTML  

    In this paper, we propose a hyperspectral image denoising algorithm with a Spatial-spectral view fusion strategy. The idea is to denoise a noisy hyperspectral 3-D cube using the hyperspectral total variation algorithm, but applied to both the spatial and spectral views. A metric Q-weighted fusion algorithm is then adopted to merge the denoising results of the two views together, so that the denoising result is improved. A number of experiments illustrate that the proposed approach can produce a better denoising result than both the individual spatial and spectral view denoising results. View full abstract»

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  • Two-Dimensional Radar Backscattering Modeling of Oil Slicks at Sea Based on the Model of Local Balance: Validation of Two Asymptotic Techniques for Thick Films

    Publication Year: 2014 , Page(s): 2326 - 2338
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2441 KB) |  | HTML iconHTML  

    The problem of hydrodynamic modeling of the surfaces of oil films at sea is treated by using physical models, namely, the model of local balance and the Elfouhaily spectrum model for describing the clean sea surface. Then, this refined hydrodynamic modeling of the surfaces of contaminated seas makes it possible to derive electromagnetic modeling by considering thin oil films on the sea surface. Two simplifying approaches in dealing with this complex double-layer problem are described, called “thin-layer” and “classical” approaches. These two approaches, both having the advantage of reducing to a single-layer problem, are compared with a rigorous reference method for 2-D problems. Thus, their validity domains are analyzed in terms of incidence angle, wind speed, polarization, frequency, and oil viscosity. Finally, the polarimetric behavior of both clean and contaminated seas is analyzed; following recent work led on satellite measurements, the same features are retrieved, and the influence of incidence angle, frequency, and oil viscosity can be studied. View full abstract»

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  • Variations of the Tropopause Over Different Latitude Bands Observed Using COSMIC Radio Occultation Bending Angles

    Publication Year: 2014 , Page(s): 2339 - 2349
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1239 KB) |  | HTML iconHTML  

    The tropopause is a transition layer between the troposphere and the stratosphere. The exchange of air, water vapor, trace gases, and energy between the troposphere and the stratosphere occurs in this layer. Accurate and continuous observations of the tropopause on a global scale are crucial for monitoring stratosphere-troposphere exchange and understanding the properties of atmosphere in the upper troposphere and lower stratosphere. In this paper, the tropopause heights are identified from Global Positioning System radio occultation (RO) bending angle profiles using the covariance transform method. Temporal variations of the tropopause parameters, including the tropopause height, temperature, and pressure at different latitude bands are investigated using the RO observations from the Constellation Observing System for Meteorology, Ionosphere and Climate mission during the period from January 2007 to December 2011. We divide the Earth into 18 nonoverlapping latitude bands 10 ° wide. Monthly averages of the tropopause parameters weighted by area are calculated at each latitude band and the temporal variations of these tropopause parameters are analyzed. The results indicate that the latitudinal variation patterns of the tropopause parameters in the Northern Hemisphere are different than those in the Southern Hemisphere. The relationship between the variations of different tropopause parameters is studied. The results show that the variation of the tropopause temperature and pressure is negatively correlated with that of the tropopause height in most of the latitude bands. In addition, the trend of the variation of the tropopause height in each latitude band is calculated with the median of pairwise slopes regression method. We find that the overall trend in the tropopause height varies in different latitude bands. The global average tropopause height decreases ~ 7 m/a during the period from 2007 to 2011. View full abstract»

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  • Precipitation Classification Using Measurements From Commercial Microwave Links

    Publication Year: 2014 , Page(s): 2350 - 2356
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (872 KB) |  | HTML iconHTML  

    Commercial wireless microwave links have been recently proven to be an effective tool for precipitation monitoring, mainly for accurate rainfall estimation and high-resolution rainfall mapping. This paper focuses on the challenge of precipitation classification from the measurements of received signal level (RSL) in several commercial wireless microwave links, by suggesting a tree of classification based on the physical features that distinguish between different phenomena. Wet periods are first identified, followed by a classification of the wet periods into pure rain or sleet. The classification is based on the kernel Fisher discriminant analysis, followed by a decision-making process. The suggested procedure is tested on real data, and its performance is evaluated. It is shown that the proposed classification is in very good agreement (85%) with that of a special-purpose meteorological device called disdrometer. View full abstract»

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  • Optical Signal Processor for Millimeter-Wave Interferometric Radiometry

    Publication Year: 2014 , Page(s): 2357 - 2368
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1962 KB) |  | HTML iconHTML  

    In interferometric radiometry, the correlations between all pairs of radio-frequency (RF) receivers must be performed in order to obtain the scene visibilities. This represents a cumbersome problem in passive imaging where large signal bandwidths are typically required to achieve fast-acquisition times and improve the radiometric resolution of the image. In this case, the signal distribution and the correlation at intermediate frequencies require very fast signal acquisition and processing subsystems. This paper presents a technique to perform correlations of millimeter-wave signals produced by thermal emission in the optical domain as a solution to the aforementioned problem. The proposed method is based on converting the RF signal to the optical domain by modulating a laser beam with the RF signal using a LiNbO3 phase modulator. This conversion allows to perform the signal distribution in the optical domain to obtain the combination of the receiver pairs. The correlation is obtained by measuring the power of the photocurrent produced by photodetecting the combined signal. The results of an experimental validation consisting on the acquisition of passive images using a linear interferometric array are presented to support the feasibility of the method. In addition, performance considerations of the system have been developed and validated by calculating the standard deviation of a visibility measurement. View full abstract»

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  • High-Resolution Radar Imaging of Space Targets Based on HRRP Series

    Publication Year: 2014 , Page(s): 2369 - 2381
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (819 KB) |  | HTML iconHTML  

    When wide or ultrawideband, low pulse repetition frequency (PRF) radar is applied to the imaging of space targets; it is highly possible that motion through range cell and azimuth under-sampling occurs, which will result in image smearing. To figure out this problem, this paper proposes a novel, three-step imaging method using the high-resolution range profile (HRRP) series. In the first step, high-quality HRRP series are obtained based on the theory of sparse signal representation. Then, based on the Kalman predictor and the minimum Euclidean distance criterion, motion and amplitude feature-based scatterer trajectory association is carried out to form the scatterer trajectory matrix, from which the scatterer locations are conveniently solved in the last step. Compared to the traditional imaging techniques based on Doppler analysis, the proposed method is able to mitigate the influence of azimuth under-sampling, and may provide a new solution to high-resolution imaging of targets moving nonuniformly in low PRF scenarios. Finally, simulations have proved the effectiveness of the proposed method. View full abstract»

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  • Multilevel Image Segmentation Based on Fractional-Order Darwinian Particle Swarm Optimization

    Publication Year: 2014 , Page(s): 2382 - 2394
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2385 KB) |  | HTML iconHTML  

    Hyperspectral remote sensing images contain hundreds of data channels. Due to the high dimensionality of the hyperspectral data, it is difficult to design accurate and efficient image segmentation algorithms for such imagery. In this paper, a new multilevel thresholding method is introduced for the segmentation of hyperspectral and multispectral images. The new method is based on fractional-order Darwinian particle swarm optimization (FODPSO) which exploits the many swarms of test solutions that may exist at any time. In addition, the concept of fractional derivative is used to control the convergence rate of particles. In this paper, the so-called Otsu problem is solved for each channel of the multispectral and hyperspectral data. Therefore, the problem of n-level thresholding is reduced to an optimization problem in order to search for the thresholds that maximize the between-class variance. Experimental results are favorable for the FODPSO when compared to other bioinspired methods for multilevel segmentation of multispectral and hyperspectral images. The FODPSO presents a statistically significant improvement in terms of both CPU time and fitness value, i.e., the approach is able to find the optimal set of thresholds with a larger between-class variance in less computational time than the other approaches. In addition, a new classification approach based on support vector machine (SVM) and FODPSO is introduced in this paper. Results confirm that the new segmentation method is able to improve upon results obtained with the standard SVM in terms of classification accuracies. View full abstract»

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  • A Comparison of Microwave Window Channel Retrieved and Forward-Modeled Emissivities Over the U.S. Southern Great Plains

    Publication Year: 2014 , Page(s): 2395 - 2412
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2639 KB) |  | HTML iconHTML  

    An accurate understanding of land surface emissivity in terms of associated surface properties is necessary for improved passive microwave remote sensing of the atmosphere, including water vapor, clouds, and precipitation, over land. In an effort to advance this understanding, emissivities are calculated for a 5 ° latitude by 5 ° longitude region in the U.S. Southern Great Plains using a combination of land surface model and physical emissivity model. Results are compared to retrieved values from the Advanced Microwave Scanning Radiometer-Earth Observing System passive microwave observations for cloud-free scenes over a six-year period. The resulting emissivities are compared in the context of surface properties including surface temperature, leaf area index (LAI), soil moisture, and precipitation. The comparison confirms that lower frequency channels respond most directly to the surface soil and its dielectric properties. Differences between retrieved and modeled emissivities are generally lower than 2%-3% and appear to be a function of soil moisture and LAI at frequencies less than 37 GHz. Agreement is better for the vertical polarization channels. At 89 GHz, a large difference is present between retrieved and modeled emissivities in both mean and magnitude of variability, particularly in the summer months. Problems are likely present at higher microwave frequencies in both the retrieved and modeled products, including the inability of the emissivity model to represent liquid water in the form of dew or precipitation interception on the vegetation canopy. View full abstract»

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  • Unified Design of a Feature-Based ADAC System for Mine Hunting Using Synthetic Aperture Sonar

    Publication Year: 2014 , Page(s): 2413 - 2426
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3727 KB) |  | HTML iconHTML  

    A system for automatic detection and classification (ADAC) of underwater objects for mine hunting applications is proposed. The system consists of three steps: segmentation, feature extraction, and classification. This paper focuses on two design issues: the selection of the optimal classifier and the selection of the optimal feature subset. Often, the comparison of classification systems is based on a pre-selected feature set. However, a different subset might yield a different ranking. We apply a resampling algorithm that assesses the classifier performance without constraints to any specific feature subset. Once a classifier is chosen, a feature selection algorithm estimates the optimal feature subset. We propose a novel extension of the sequential forward selection (SFS) and the sequential forward floating selection (SFFS) methods, which mitigates their main limitations, i.e., the nesting problem. Instead of keeping the best alternative at each iteration, a set of D options is stored. The performance of the so-called D-SFS and D-SFFS is tested on simulated and real data, significantly outperforming the standard algorithms. The proposed methods are also used for designing an ADAC system for mine hunting based on two extensive databases of synthetic aperture sonar images. View full abstract»

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  • Fully Automatic Dark-Spot Detection From SAR Imagery With the Combination of Nonadaptive Weibull Multiplicative Model and Pulse-Coupled Neural Networks

    Publication Year: 2014 , Page(s): 2427 - 2435
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1790 KB) |  | HTML iconHTML  

    Dark-spot detection is a critical step in oil-spill detection. In this paper, a novel approach for automated dark-spot detection using synthetic aperture radar imagery is presented. A new approach from the combination of Weibull multiplicative model (WMM) and pulse-coupled neural network (PCNN) techniques is proposed to differentiate between the dark spots and the background. First, the filter created based on WMM is applied to each subimage. Second, the subimage is segmented by PCNN techniques. As the last step, a very simple filtering process is used to eliminate the false targets. The proposed approach was tested on 60 Envisat and ERS2 images which contained dark spots. The same parameters were used in all tests. For the overall data set, an average accuracy of 93.66% was obtained. The average computational time for dark-spot detection with a 512 × 512 image is about 7 s using IDL software, which is the fastest one in this field at present. Our experimental results demonstrate that the proposed approach is very fast, robust, and effective. The proposed approach can be applied on any kind of synthetic aperture radar imagery. View full abstract»

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  • Atmospheric Phase Screen Compensation in Ground-Based SAR With a Multiple-Regression Model Over Mountainous Regions

    Publication Year: 2014 , Page(s): 2436 - 2449
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2465 KB) |  | HTML iconHTML  

    In this paper, a new model-based technique for the compensation of severe height-dependent atmospheric artifacts, using ground-based synthetic aperture radar (SAR) data over mountainous regions, is proposed. The method presented represents an extension of already existing techniques, but now taking into account the effect of steep topography in the atmospheric phase screen compensation process. In addition, the technique is adapted to work with polarimetric SAR data, showing, in that case, a noticeable improvement in the compensation process. The method is validated in the mountainous environment of El Forn de Canillo, located in the Andorran Pyrenees, where there is a slow-moving landslide that nowadays is being reactivated coinciding with strong rain episodes. In this framework, ten zero-baseline fully polarimetric data sets have been acquired at X-band during a one-year measurement campaign (October 2010-October 2011) with the GB-SAR sensor developed at the Universitat Politècnica de Catalunya. First, the impact of the severe atmospheric fluctuations among multitemporal GB-SAR measurements is carefully studied and analyzed. Hence, the need to correctly estimate and compensate the resulting phase differences when retrieving interferometric information is put forward in the frame of differential-SAR-interferometry applications. View full abstract»

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  • Two-Dimensional TSVD to Enhance the Spatial Resolution of Radiometer Data

    Publication Year: 2014 , Page(s): 2450 - 2458
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1408 KB) |  | HTML iconHTML  

    A reconstruction technique, based on the 2-D truncated singular value decomposition, is first proposed to enhance the spatial resolution of radiometer earth observation measurements. The technique is very computer time effective when the kernel is a 2-D tensor product. The key issue regarding the selection of the truncation parameter is addressed by the statistically based generalized cross-validation approach. Experiments undertaken on a data set consisting of both simulated and actual 2-D special sensor microwave imager radiometer measurements show the robustness of the technique against the additive noise and its effectiveness in terms of processing time. A typical 2-D radiometer scene is processed in seconds by a standard PC processor. View full abstract»

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  • Hybrid Approach for Unbiased Coherence Estimation for Multitemporal InSAR

    Publication Year: 2014 , Page(s): 2459 - 2473
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1823 KB) |  | HTML iconHTML  

    The coherence of radar echoes is a fundamental observable in interferometric synthetic aperture radar (InSAR) measurements. It provides a quantitative measure of the scattering properties of imaged surfaces and therefore is widely applied to study the physical processes of the Earth. However, unfortunately, the estimated coherence values are often biased due to various reasons such as radar signal nonstationarity and the bias in the estimators used. In this paper, we focus on multitemporal InSAR coherence estimation and present a hybrid approach that mitigates effectively the errors in the estimation. The proposed approach is almost completely self-adaptive and workable for both Gaussian and non-Gaussian SAR scenes. Moreover, the bias of the sample coherence can be mitigated with even only several samples included for a given pixel. Therefore, it is a more pragmatic method for accurate coherence estimation and can be applied actually. Different data sets are used to test the proposed method and demonstrate its advantages. View full abstract»

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  • Generalized Polarimetric Model-Based Decompositions Using Incoherent Scattering Models

    Publication Year: 2014 , Page(s): 2474 - 2491
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3342 KB) |  | HTML iconHTML  

    The model-based scattering decomposition pioneered by Freeman and Durden has stimulated research in characterizing polarimetric synthetic aperture radar (PolSAR) scattering phenomenon and its applications. The Freeman and Durden decomposition as originally developed is based on three scattering models for volume, surface, and double bounce. It is known that the decomposition often produces negative scattering powers for a large number of pixels. This implies that the scattering models are inconsistent with the data. In this paper, we investigate the model deficiency problem and propose several algorithms to mitigate it. To achieve this, we developed an incoherent scattering model based on the polarization orientation angle distribution of phase differences. Two approaches are taken to reduce the number of negative power pixels: 1) adopt a volume scattering model by including variable shape factor while keeping the original surface and double bounce models unchanged, and 2) incorporate the incoherent surface or double bounce model while keeping the original volume model. In addition, the combination of 1) and 2) is explored, and the effect of polarization orientation compensation on these algorithms is investigated. The effectiveness of these approaches is compared using L-band AIRSAR and E-SAR PolSAR data. It will be shown that model efficiency is improved with occurrence of negative power reduced to an insignificant level. View full abstract»

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  • Active Learning in the Spatial Domain for Remote Sensing Image Classification

    Publication Year: 2014 , Page(s): 2492 - 2507
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1906 KB) |  | HTML iconHTML  

    Active learning (AL) algorithms have been proven useful in reducing the number of required training samples for remote sensing applications; however, most methods query samples pointwise without considering spatial constraints on their distribution. This may often lead to a spatially dispersed distribution of training points unfavorable for visual image interpretation or field surveys. The aim of this study is to develop region-based AL heuristics to guide user attention toward a limited number of compact spatial batches rather than distributed points. The proposed query functions are based on a tree ensemble classifier and combine criteria of sample uncertainty and diversity to select regions of interest. Class imbalance, which is inherent to many remote sensing applications, is addressed through stratified bootstrap sampling. Empirical tests of the proposed methods are performed with multitemporal and multisensor satellite images capturing, in particular, sites recently affected by large-scale landslide events. The assessment includes an experimental evaluation of the labeling time required by the user and the computational runtime, and a sensitivity analysis of the main algorithm parameters. Region-based heuristics that consider sample uncertainty and diversity are found to outperform pointwise sampling and region-based methods that consider only uncertainty. Reference landslide inventories from five different experts enable a detailed assessment of the spatial distribution of remaining errors and the uncertainty of the reference data. View full abstract»

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  • Simultaneous Measurements by Advanced SAR and Radar Altimeter on Potential Improvement of Ocean Wave Model Assimilation

    Publication Year: 2014 , Page(s): 2508 - 2518
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1823 KB) |  | HTML iconHTML  

    Simultaneous measurements of significant wave height (SWH) obtained from two independent active microwave sensors of Radar Altimeter 2 (RA-2) and Advanced Synthetic Aperture Radar (ASAR) onboard the ENVIronmental SATellite (ENVISAT) are used for a global verification of ocean wave models (WAMs). In the present study, SWH is retrieved from ASAR wave mode data using the empirical algorithm called C-band WAVE algorithm for ENVISAT, which is capable of representing total SWH irrespective of the cutoff of SAR. Comparisons of two radar measurements with the reanalyses ERA-Interim model (with assimilation of RA-2 measurements) and the German operational WAM (Deutscher Wetterdienst Global Sea wave Model, without assimilation before 2008) show that both WAMs agree well with ASAR and RA-2 measurements. However, the discrepancies of agreement indicate to which extent that the assimilation of RA-2 measurements can improve the performance of WAMs. Moreover, differences in the comparisons of ASAR and RA-2 measurements with the same WAM of ERA-Interim reveal that, although assimilation of RA-2 significantly improves the accuracy of model on grids near the RA-2 tracks, the improvement decreases along with the increase of distance between model grids and RA-2 tracks. View full abstract»

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  • Retrieval and Quality Assessment of Wind Velocity Vectors on the Ocean With C-Band SAR

    Publication Year: 2014 , Page(s): 2519 - 2537
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4656 KB) |  | HTML iconHTML  

    Wind vector fields derived from synthetic aperture radar (SAR) sensors show variations at smaller scales than most other globally available surface wind sources. However, few studies have been devoted to the investigation of the accuracy of SAR-derived wind fields at different scales and how they compare with other wind data. In order to investigate these issues, an algorithm for the retrieval of SAR-derived wind vectors has been developed, and a quality assessment between the retrievals and in situ, scatterometer, and numerical weather model (NWM) wind data has been performed. The implemented wind retrieval algorithm detects streak features in the SAR image to estimate wind directions and inverts wind speeds using CMOD-IFR2, CMOD5, or CMOD5.N geophysical model functions. In addition, a regularization method for filtering outliers in the wind direction retrievals is used. Retrievals compared with in situ data indicated better performance at offshore locations for wind speed inversions with CMOD5.N. The bias and standard deviation for offshore regularized wind directions and CMOD5.N speeds are 9° and 25° and -0.1 and 1.4 m/s, respectively. The comparison with the scatterometer and NWM wind data has been performed for retrievals at 5-, 10-, and 20-km resolution. The results indicate a better agreement of the coarser retrievals with the reference data. Nevertheless, mapping of smaller scale features requires wind directions from the SAR image itself. View full abstract»

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  • Can SMOS Data be Used Directly on the 15-km Discrete Global Grid?

    Publication Year: 2014 , Page(s): 2538 - 2544
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1498 KB) |  | HTML iconHTML  

    Radiometric observations from the Soil Moisture and Ocean Salinity (SMOS) mission are processed to Level 1 brightness temperature (Tb) with ~ 42-km spatial resolution and reported on a 15-km hexagonal Discrete Global Grid (DGG). While these data should be used at the 42-km resolution which the oversampled DGG represents, this paper poses the question of whether they can be used directly at 15-km resolution without undertaking downscaling or implementing multiscale-type procedures when used in data assimilation. To assess the error associated with using the 42-km SMOS Tb data at 15-km resolution, this study employs 1-km Tb data from the Australian Airborne Cal/Val Experiment for SMOS (AACES). The study compares SMOS-like data derived from AACES at 42-km resolution with Tb values actually observed on the 15-km DGG. These 15-km DGG data are subsequently interpolated to a regular 12-km model grid and compared with actual observations at that resolution. The results show that the average root mean square differences in Tb between the 15- and 42-km footprints are 4.5 K and 3.9 K for horizontal (H) and vertical (V) polarizations, respectively, with a maximum difference of 12.9 K. The errors when interpolating the 42-km data onto the 12-km model grid were estimated to be 3.3 K for H polarization and 2.9 K for V polarization under the assumption of independence or 4.5 K and 3.9 K for H and V polarizations, respectively, with 4.0 K in H polarization and 3.6 K in V polarization from the 15- to 12-km interpolation process alone. An evaluation of the Tb differences for 42-km data assumed on the 15-km DGG found no correlation with vegetation based on leaf area index and only slight correlation with the spatial variance of SMOS data and topographic roughness. Given these differences and the noise that currently exists in SMOS Tb at 42 km, the 15-km DGG data can be used directly on the hexagonal grid or interpolated- onto a regular grid of equivalent spatial resolution without further degrading the data quality. View full abstract»

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  • Shadow Detection and Reconstruction in High-Resolution Satellite Images via Morphological Filtering and Example-Based Learning

    Publication Year: 2014 , Page(s): 2545 - 2554
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1588 KB) |  | HTML iconHTML  

    The shadows in high-resolution satellite images are usually caused by the constraints of imaging conditions and the existence of high-rise objects, and this is particularly so in urban areas. To alleviate the shadow effects in high-resolution images for their further applications, this paper proposes a novel shadow detection algorithm based on the morphological filtering and a novel shadow reconstruction algorithm based on the example learning method. In the shadow detection stage, an initial shadow mask is generated by the thresholding method, and then, the noise and wrong shadow regions are removed by the morphological filtering method. The shadow reconstruction stage consists of two phases: the example-based learning phase and the inference phase. During the example-based learning phase, the shadow and the corresponding nonshadow pixels are first manually sampled from the study scene, and then, these samples form a shadow library and a nonshadow library, which are correlated by a Markov random field (MRF). During the inference phase, the underlying land-cover pixels are reconstructed from the corresponding shadow pixels by adopting the Bayesian belief propagation algorithm to solve the MRF. Experimental results on QuickBird and WorldView-2 satellite images have demonstrated that the proposed shadow detection algorithm can generate accurate and continuous shadow masks and also that the estimated nonshadow regions from the proposed shadow reconstruction algorithm are highly compatible with their surrounding nonshadow regions. Finally, we examine the effects of the reconstructed image on the application of classification by comparing the classification maps of images before and after shadow reconstruction. View full abstract»

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  • Equivalent Number of Scatterers for SAR Speckle Modeling

    Publication Year: 2014 , Page(s): 2555 - 2564
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    In this paper, the equivalent number of scatterers of a rough scattering surface is defined, physically justified, and evaluated. New-generation spaceborne synthetic aperture radar (SAR) sensors are acquiring images at such a high ground resolution that, quite often, the statistics of these images do not match with those predicted by the classical Rayleigh speckle model. Non-Rayleigh speckle is frequently mathematically modeled via K-distribution in terms of a parameter that presently can be estimated (i.e., a posteriori) on the SAR images and is linked to the number of scatterers per resolution cell. However, to model and predict (i.e., a priori) the statistical behavior of the SAR images, a full characterization of the scatterers is required. To this aim, the concept of equivalent number of scatterers of a rough scattering surface is here defined and physically justified. This parameter is then analytically evaluated in closed form as a function of the roughness of the illuminated surface and of SAR sensor parameters. The presented analytical evaluation applies to both classical and fractal descriptions of the surface roughness. Finally, the dependence of the equivalent number of scatterers on the roughness of the illuminated surface and on SAR sensor parameters is analyzed for a range of values of roughness parameters actually encountered in natural surfaces and by considering typical system parameters of modern high-resolution spaceborne SAR systems. It is shown that, actually, for some combinations of realistic surface and system parameters, the equivalent number of scatterers can be on the order of unity. View full abstract»

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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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Antonio J. Plaza
University of Extremadura