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Geoscience and Remote Sensing, IEEE Transactions on

Issue 4 • Date April 2011

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  • [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): 1173 - 1174
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  • Foreword to the Special Issue on Remote Sensing and Modeling of Surface Properties

    Page(s): 1175 - 1176
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  • Evaluating an Improved Parameterization of the Soil Emission in L-MEB

    Page(s): 1177 - 1189
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (461 KB) |  | HTML iconHTML  

    In the forward model [L-band microwave emission of the biosphere (L-MEB)] used in the Soil Moisture and Ocean Salinity level-2 retrieval algorithm, modeling of the roughness effects is based on a simple semiempirical approach using three main “roughness” model parameters: HR, QR, and NR. In many studies, the two parameters QR and NR are set to zero. However, recent results in the literature showed that this is too approximate to accurately simulate the microwave emission of the rough soil surfaces at L-band. To investigate this, a reanalysis of the PORTOS-93 data set was carried out in this paper, considering a large range of roughness conditions. First, the results confirmed that QR could be set to zero. Second, a refinement of the L-MEB soil model, considering values of NR for both polarizations (namely, NRV and NRH), improved the model accuracy. Furthermore, simple calibrations relating the retrieved values of the roughness model parameters HR and (NRH - NRV) to the standard deviation of the surface height were developed. This new calibration of L-MEB provided a good accuracy (better than 5 K) over a large range of soil roughness and moisture conditions of the PORTOS-93 data set. Conversely, the calibrations of the roughness effects based on the Choudhury approach, which is still widely used, provided unrealistic values of surface emissivities for medium or large roughness conditions. View full abstract»

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  • Sensitivity of Passive Microwave Observations to Soil Moisture and Vegetation Water Content: L-Band to W-Band

    Page(s): 1190 - 1199
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    Ground-based multifrequency (L-band to W-band, 1.41-90 GHz) and multiangular (20°-50°) bipolarized (V and H) microwave radiometer observations, acquired over a dense wheat field, are analyzed in order to assess the sensitivity of brightness temperatures (Tb) to land surface properties: surface soil moisture (mv) and vegetation water content (VWC). For each frequency, a combination of microwave Tb observed at either two contrasting incidence angles or two polarizations is used to retrieve mv and VWC, through regressed empirical logarithmic equations. The retrieval performance of the regression is used as an indicator of the sensitivity of the microwave signal to either mv or VWC. In general, L-band measurements are shown to be sensitive to both mv and VWC, with lowest root mean square errors (0.04 m3 ·m-3 and 0.52 kg ·m-2 , respectively) obtained at H polarization, 20° and 50° incidence angles. In spite of the dense vegetation, it is shown that mv influences the microwave observations from L-band to K-band (23.8 GHz). The highest sensitivity to soil moisture is observed at L-band in all configurations, while observations at higher frequencies, from C-band (5.05 GHz) to K-band, are only moderately influenced by mv at low incidence angles (e.g., 20°). These frequencies are also shown to be very sensitive to VWC in all the configurations tested. The highest frequencies (Q- and W-bands) are shown to be moderately sensitive to VWC only. These results are used to analyze the response of W-band emissivities derived from the Advanced Microwave Sounding Unit instruments over northern France. View full abstract»

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  • Multiscale Analysis of Topographic Surface Roughness in the Midland Valley, Scotland

    Page(s): 1200 - 1213
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    Surface roughness is an important geomorphological variable which has been used in the Earth and planetary sciences to infer material properties, current/past processes, and the time elapsed since formation. No single definition exists; however, within the context of geomorphometry, we use surface roughness as an expression of the variability of a topographic surface at a given scale, where the scale of analysis is determined by the size of the landforms or geomorphic features of interest. Six techniques for the calculation of surface roughness were selected for an assessment of the parameter's behavior at different spatial scales and data-set resolutions. Area ratio operated independently of scale, providing consistent results across spatial resolutions. Vector dispersion produced results with increasing roughness and homogenization of terrain at coarser resolutions and larger window sizes. Standard deviation of residual topography highlighted local features and did not detect regional relief. Standard deviation of elevation correctly identified breaks of slope and was good at detecting regional relief. Standard deviation of slope (SDslope) also correctly identified smooth sloping areas and breaks of slope, providing the best results for geomorphological analysis. Standard deviation of profile curvature identified the breaks of slope, although not as strongly as SDslope, and it is sensitive to noise and spurious data. In general, SDslope offered good performance at a variety of scales, while the simplicity of calculation is perhaps its single greatest benefit. View full abstract»

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  • Electromagnetic Land Surface Classification Through Integration of Optical and Radar Remote Sensing Data

    Page(s): 1215 - 1223
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    We present a nonhierarchical electromagnetic (EM) land surface classification method through the integration of satellite multispectral high-resolution optical and polarized radar images of central Alberta near the Saskatchewan border. We implement a conventional supervised land surface classification method and a principal component analysis to a QuickBird image. The EM properties are then assigned to the classified surfaces to produce hierarchical EM land classification maps. To further classify a hierarchical EM surface (i.e., dielectric constant), we calculate the root-mean-square surface height with a Shuttle Radar Topography Mission 3-arc-second digital elevation model and the temperatures from a thermal band of a Landsat-5 Thematic Mapper image. We also calculate the backscattering coefficients from the Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar image. Using these estimated values, we calculate the intrinsic weighting factors with the Dubois (1995) model for less vegetated land areas and the Ulaby (1986) model for open water areas. By applying these weighting factors to the hierarchical EM surface, we generate a nonhierarchical higher resolution EM surface map of the study area. View full abstract»

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  • Sea Ice Emissivities and Effective Temperatures at MHS Frequencies: An Analysis of Airborne Microwave Data Measured During Two Arctic Campaigns

    Page(s): 1223 - 1237
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    Satellite-based sounding of the temperature and humidity of the lower troposphere is only carried out over open sea surfaces because of large uncertainties in the surface emissivity and effective emitting temperature of other surfaces. The study of sea ice and snow surface emissivities at Microwave Humidity Sounder (MHS) frequencies has been the focus of two airborne campaigns carried out by the Met Office over the past decade to promote the assimilation of lower tropospheric sounding radiances over the polar regions. The Polar Experiment (POLEX) campaign occurred during March 2001. Five flights were carried out over the Arctic Ocean north of Svalbard. The Cold Land Processes II (CLPX-II) campaign took place during February 2008. The sea ice portion of CLPX-II consisted of four flights sampling the emissivities and effective temperatures in the Chukchi and Beaufort Seas and frozen wetlands near Barrow, Alaska. This paper summarizes the findings of a recent analysis from the CLPX-II and POLEX campaigns. First, time series of Lambertian emissivities and effective temperatures at MHS frequencies are retrieved for each flight. These time series and the satellite imagery are then used to classify the surfaces over flown. The behaviors of the emissivity spectra with changing surface type are discussed in terms of changes in the ice and snow conditions on the surface. The difference between 89- and 157-GHz emissivities is found to be related to both the snow depth and the relative amounts of depth hoar and wind slab within the snowpack. View full abstract»

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  • An Improved Fast Microwave Water Emissivity Model

    Page(s): 1238 - 1250
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    Satellite measurements from microwave instruments have made a significant contribution to the skill of numerical weather forecasting, on both global and regional scales. A FAST microwave Emissivity Model (FASTEM), which was developed by the Met Office, U.K., has been widely utilized to compute the surface emitted radiation in forward calculations. However, the FASTEM model was developed for frequencies in the range of 20-60 GHz, and it is biased at higher and lower frequencies. Several critical components such as variable sea surface salinity and full Stokes vector have not been generally taken into account. In this paper, the effects of the permittivity models are investigated, and a new permittivity model is generated by using the measurements for fresh and salt water at frequencies between 1.4 and 410 GHz. A modified sea surface roughness model from Durden and Vesecky is applied to the detailed two-scale surface emissivity calculations. This ocean emissivity model at microwave is now being used in the Community Radiative Transfer Model, and it has resulted in some major improvements in microwave radiance simulations. This paper is a joint effort of the Met Office, U.K., and the Joint Center for the Satellite Data Assimilation, U.S. The model is called as FASTEM-4 in the Radiative Transfer for TIROS Operational Vertical Sounder model. View full abstract»

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  • Potential Use of Surface-Sensitive Microwave Observations Over Land in Numerical Weather Prediction

    Page(s): 1251 - 1262
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    This paper describes several sensitivity studies carried out with the French global 4-D-Var system to check its ability to assimilate surface-sensitive observations over land from the Special Sensor Microwave Imager (SSM/I). As well as a sound knowledge of land-surface parameters, the assimilation of SSM/I observations requires effective rain-detection and bias-correction algorithms. Three sensitivity components are hence analyzed with a special emphasis on the land-surface emissivity at SSM/I frequencies estimated from satellite observations. Several rain algorithms were tested to reject cloudy/rainy observations over land, and the bias-correction scheme was adapted to improve its performance over land and sea surfaces. Once these problems have been outlined, a global 4-D-Var assimilation experiment which assimilates SSM/I observations over land surfaces was run and compared with a control experiment. The impact on forecast scores has been found to be globally positive. Nevertheless, the very high sensitivity of SSM/I to each of the three components presented in this study is characterized by opposite effects that, once clustered together, lead to some residual biases over land due to their combined effects. View full abstract»

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  • Effects of Microwave Desert Surface Emissivity on AMSU-A Data Assimilation

    Page(s): 1263 - 1276
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    A microwave land emissivity library has been developed from the Advanced Microwave Sounding Unit (AMSU) data for improving satellite data assimilation. Over the desert, surface emissivity is classified according to soil type into several spectra. For sand, loamy sand, and sandy loam, which contain some large mineral particles, the emissivity spectra generally decrease with frequency. For other desert types whose compositions are dominated by mineral particles smaller than a few hundred micrometers, the emissivity values are almost constant or slightly increasing with frequency. These emissivity features are consistent with those from the land emissivity data set developed at Météo-France. Moreover, both the emissivity library and the Météo-France data set are applied to the assimilation of the AMSU-A data in the National Centers for Environmental Prediction Global Forecast System (GFS). In comparison with the microwave land emissivity model previously developed by Weng , both the emissivity library and the Météo-France data set improve the utilization of the AMSU-A data in the GFS. The increased use of the AMSU-A data through the emissivity library or the data set results in positive impacts on the global medium-range forecasts over either the Southern or Northern Hemispheres. View full abstract»

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  • Global Land Surface Emissivity Retrieved From Satellite Ultraspectral IR Measurements

    Page(s): 1277 - 1290
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    Ultraspectral resolution infrared (IR) radiances obtained from nadir observations provide information about the atmosphere, surface, aerosols, and clouds. Surface spectral emissivity (SSE) and surface skin temperature from current and future operational satellites can and will reveal critical information about the Earth's ecosystem and land-surface-type properties, which might be utilized as a means of long-term monitoring of the Earth's environment and global climate change. In this study, fast radiative transfer models applied to the atmosphere under all weather conditions are used for atmospheric profile and surface or cloud parameter retrieval from ultraspectral and/or hyperspectral spaceborne IR soundings. An inversion scheme, dealing with cloudy as well as cloud-free radiances observed with ultraspectral IR sounders, has been developed to simultaneously retrieve atmospheric thermodynamic and surface or cloud microphysical parameters. This inversion scheme has been applied to the Infrared Atmospheric Sounding Interferometer (IASI). Rapidly produced SSE is initially evaluated through quality control checks on the retrievals of other impacted surface and atmospheric parameters. Initial validation of retrieved emissivity spectra is conducted with Namib and Kalahari desert laboratory measurements. Seasonal products of global land SSE and surface skin temperature retrieved with IASI are presented to demonstrate seasonal variation of SSE. View full abstract»

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  • Temperature and Emissivity Retrievals From Hyperspectral Thermal Infrared Data Using Linear Spectral Emissivity Constraint

    Page(s): 1291 - 1303
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    Owing to the ill-posed problem of radiometric equations, the separation of land surface temperature (LST) and land surface emissivity (LSE) from observed data has always been a troublesome problem. On the basis of the assumption that the LSE spectrum can be described by a piecewise linear function, a new method has been proposed to retrieve LST and LSE from atmospherically corrected hyperspectral thermal infrared data using linear spectral emissivity constraint. Comparisons with the existing methods found in literature show that our proposed method is more noise immune than the existing methods. Even with a NEΔT of 0.5 K, the rmse of LST is observed to be only 0.16 K, and that of LSE is 0.006. In addition, our proposed method is simple and efficient and does not encounter the problem of singular values unlike the existing methods. As for the impact of the atmosphere, the results show that our proposed method performs well with the uncertainty of the atmospheric downwelling radiance but suffers from the inaccuracy of the atmospheric upwelling radiance and atmospheric transmittance, which implies that an accurate atmospheric correction is still needed to convert the radiance measured at the satellite level to the at-ground radiance. To validate the proposed method, a field experiment was conducted, and the results show that 80% of the samples have an accuracy of LST within 1 K and that the mean values of LSE are accurate to 0.01. View full abstract»

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  • Generating Consistent Land Surface Temperature and Emissivity Products Between ASTER and MODIS Data for Earth Science Research

    Page(s): 1304 - 1315
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    Land surface temperature and emissivity (LST&E) products are generated by the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on the National Aeronautics and Space Administration's Terra satellite. These products are generated at different spatial, spectral, and temporal resolutions, resulting in discrepancies between them that are difficult to quantify, compounded by the fact that different retrieval algorithms are used to produce them. The highest spatial resolution MODIS emissivity product currently produced is from the day/night algorithm, which has a spatial resolution of 5 km. The lack of a high-spatial-resolution emissivity product from MODIS limits the usefulness of the data for a variety of applications and limits utilization with higher resolution products such as those from ASTER. This paper aims to address this problem by using the ASTER Temperature Emissivity Separation (TES) algorithm, combined with an improved atmospheric correction method, to generate the LST&E products for MODIS at 1-km spatial resolution and for ASTER in a consistent manner. The rms differences between the ASTER and MODIS emissivities generated from TES over the southwestern U.S. were 0.013 at 8.6 μm and 0.0096 at 11 μm, with good correlations of up to 0.83. The validation with laboratory-measured sand samples from the Algodones and Kelso Dunes in CA showed a good agreement in spectral shape and magnitude, with mean emissivity differences in all bands of 0.009 and 0.010 for MODIS and ASTER, respectively. These differences are equivalent to approximately 0.6 K in the LST for a material at 300 K and at 11 μm. View full abstract»

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  • Analysis of ASTER Emissivity Product Over an Arid Area in Southern New Mexico, USA

    Page(s): 1316 - 1324
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    The accuracy of thermal infrared emissivities derived from Advanced Spaceborne Thermal Emission and Reflectance radiometer (ASTER) was assessed in an arid area in southern New Mexico, which includes the White Sands National Monument (WSNM) during 2006-2008. ASTER emissivities retrieved by the temperature and emissivity separation (TES) algorithm were directly compared with laboratory measurements of samples from WSNM. Good agreement was found for the high spectral contrast of gypsum and for the low spectral contrast of water bodies. Furthermore, the day/night consistency of ASTER emissivities was checked, and day/night emissivity differences lower than ±0.013 were observed. However, unexpected emissivity values larger than unity were retrieved by ASTER/TES at 8-9 μm , mainly concentrated over lava flow surfaces. The thermal infrared radiance image data with 90-m spatial resolution was resized to 180 m for the analysis in this paper to avoid misregistration problems due to terrain topography. Emissivity temporal variations were analyzed and attributed, in some cases, to the soil moisture variations. This was particularly noted after periods of high precipitation which occurred in August 2006. The results presented here show the high emissivity accuracy achievable with ASTER data in ideal atmospheric conditions and discuss some problems which should be considered in the future, as the retrieval of overestimated emissivity values. View full abstract»

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  • Subspace-Based Striping Noise Reduction in Hyperspectral Images

    Page(s): 1325 - 1342
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    In this paper, a new algorithm for striping noise reduction in hyperspectral images is proposed. The new algorithm exploits the orthogonal subspace approach to estimate the striping component and to remove it from the image, preserving the useful signal. The algorithm does not introduce artifacts in the data and also takes into account the dependence on the signal intensity of the striping component. The effectiveness of the algorithm in reducing striping noise is experimentally demonstrated on real data acquired both by airborne and satellite hyperspectral sensors. View full abstract»

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  • Operational and Performance Considerations of Radiative-Transfer Modeling in Hyperspectral Target Detection

    Page(s): 1343 - 1355
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    Accounting for radiative transfer within the atmosphere is usually necessary to accomplish target detection in airborne/satellite hyperspectral images. In this paper, two methods of accounting for the illumination and atmospheric effects-atmospheric compensation (AC) and forward modeling (FM)-are investigated in their application to target detection. Specifically, several crucial aspects are examined, such as the processing required, the computational complexity, and the flexibility accorded to an imperfect knowledge of acquisition conditions. Real ground-truthed hyperspectral data are employed in order to evaluate the operational applicability of such approaches in a target-detection scenario, as well as their impact on the processing-chain computational complexity. Results indicate that AC is recommended when accurate knowledge of the acquisition conditions is available, and the image has relatively uniform illumination and nonshadowed targets. Conversely, FM is preferred if scene conditions are not well known and when the targets may be subject to varying illumination conditions, including shadowing. View full abstract»

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  • A Microphysics-Based Simulator for Advanced Airborne Weather Radar Development

    Page(s): 1356 - 1373
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    Incorporating dual-polarized operation and microphysics-based processing is becoming a challenge to future scientific and commercial airborne weather radars. This paper introduces a Monte Carlo simulation-based approach to address the theoretical basis and uncertainties of hydrometeor scattering along with sensor platform properties. Detailed characterizations of mixed-phase aviation hydrometeor hazards (rain, snow, hail, and mixtures) and the impact of melting on polarimetric radar signature at X-band frequency are discussed. A “single resolution cell” Monte Carlo dual-polarization variable simulation technique is described and then applied in different radar scanning scenarios based on numeric weather prediction model output weather fields. The produced dual-polarization signatures of an X-band array radar for different scan scenarios are analyzed. View full abstract»

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  • Ground Wave Propagation Along an Inhomogeneous Rough Surface in the HF Band: Millington Effect for a Flat Earth

    Page(s): 1374 - 1382
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    In this paper, for a vertically polarized line source in the high-frequency band (3-30 MHz), a detailed analysis of the ground wave propagation over 1-D highly conducting inhomogeneous (presence of island) smooth and rough surfaces is addressed from two methods: 1) the analytical solution of Bremmer (see also Wait), which assumes that the receiver, emitter, and island heights are zeros and that the surface is composed of three smooth paths of different permittivities, and 2) the efficient rigorous banded matrix iterative approach/canonical grid (BMIA-CAG) method, which is based on the method of moments and is updated to validate the analytical solution of Bremmer. In addition, from the works of Barrick, the sea surface roughness is included in the Bremmer formulation and tested from the BMIA-CAG by considering a surface composed of sea-land-sea mixed paths. The comparisons show a good agreement between the updated BMIA-CAG (reference method) and the Bremmer analytical formulation combined with the works of Barrick. The Earth curvature is neglected. View full abstract»

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  • Improvement of Target-Detection Algorithms Based on Adaptive Three-Dimensional Filtering

    Page(s): 1383 - 1395
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1153 KB) |  | HTML iconHTML  

    Target detection is a key issue in processing hyperspectral images (HSIs). Spectral-identification-based algorithms are sensitive to spectral variability and noise in acquisition. In most cases, both the target spatial distributions and the spectral signatures are unknown, so each pixel is separately tested and appears as a target when it significantly differs from the background. In this paper, we propose two algorithms to improve the signal-to-noise ratio (SNR) of hyperspectral data, leading to detectors that are robust to noise. These algorithms consist in integrating adaptive spatial/spectral filtering into the adaptive matched filter and adaptive coherence estimator. Considering the HSIs as tensor data, our approach introduces a data representation involving multidimensional processing. It combines the advantages of spatial and spectral information using an alternating least square algorithm. To estimate the signal subspace dimension in each spatial mode, we extend the Akaike information criterion, and we develop an iterative algorithm for spectral-mode rank estimation. We demonstrate the interest of integrating the quadtree decomposition to perform an adaptive 3-D filtering and thereby preserve the local image characteristics. This leads to a significant improvement in terms of denoised tensor SNR and, consequently, in terms of detection probability. The performance of our method is exemplified using simulated and real-world HYperspectral Digital Imagery Collection Experiment images. View full abstract»

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  • Interferometric SAR Phase Filtering in the Wavelet Domain Using Simultaneous Detection and Estimation

    Page(s): 1396 - 1416
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4831 KB) |  | HTML iconHTML  

    In this paper, two interferometric SAR (InSAR) phase-filtering methods are proposed. These methods are performed in the wavelet domain and employ the simultaneous detection and estimation technique. In the wavelet domain, closed-form estimator and detector equations are derived, based upon a quadratic cost function, to minimize the combined risk of detection and estimation and, thus, the least square errors. Both methods occur within the wavelet domain; however, the first method employs the wavelet packet, while the second method is performed in the undecimated wavelet domain. A major characteristic of InSAR phase data is that the noise level is spatially variable, and the proposed methods have a particularly good comparative performance in these situations. Tests are performed using simulated phase data and show that the proposed methods have lower root-mean-square error and less noisy fringes in the filtering results than those of three existing “state-of-the-art” wavelet-domain phase-filtering methods. Tests using real InSAR data also demonstrate the superiority of the proposed methods in terms of visual and quantitative evaluation. View full abstract»

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  • Unsupervised Spatiotemporal Mining of Satellite Image Time Series Using Grouped Frequent Sequential Patterns

    Page(s): 1417 - 1430
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    An important aspect of satellite image time series is the simultaneous access to spatial and temporal information. Various tools allow end users to interpret these data without having to browse the whole data set. In this paper, we intend to extract, in an unsupervised way, temporal evolutions at the pixel level and select those covering at least a minimum surface and having a high connectivity measure. To manage the huge amount of data and the large number of potential temporal evolutions, a new approach based on data-mining techniques is presented. We have developed a frequent sequential pattern extraction method adapted to that spatiotemporal context. A successful application to crop monitoring involving optical data is described. Another application to crustal deformation monitoring using synthetic aperture radar images gives an indication about the generic nature of the proposed approach. View full abstract»

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  • Fast Computations to Electromagnetic Scattering Properties of Complex Bodies of Revolution Buried and Partly Buried in Layered Lossy Media

    Page(s): 1431 - 1440
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    An efficient finite-element method is presented to analyze the scattering from complex bodies of revolution buried or partly buried in layered lossy media. In the proposed method, high-order edge-based vector basis functions are used to expand the transverse field components and high-order node-based scalar basis functions are used to expand the angular component. A locally conformal perfectly matched layer (PML) by complex coordinate stretching for inhomogeneous medium is presented and used to truncate the computational domain. Such a kind of PML is very easy to implement in the numerical process and is able to enclose an arbitrarily shaped convex object in the spatial domain. Numerical examples are presented to demonstrate the accuracy and efficiency of the presented method. View full abstract»

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  • NL-InSAR: Nonlocal Interferogram Estimation

    Page(s): 1441 - 1452
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1453 KB) |  | HTML iconHTML  

    Interferometric synthetic aperture radar (SAR) data provide reflectivity, interferometric phase, and coherence images, which are paramount to scene interpretation or low-level processing tasks such as segmentation and 3-D reconstruction. These images are estimated in practice from a Hermitian product on local windows. These windows lead to biases and resolution losses due to the local heterogeneity caused by edges and textures. This paper proposes a nonlocal approach for the joint estimation of the reflectivity, the interferometric phase, and the coherence images from an interferometric pair of coregistered single-look complex (SLC) SAR images. Nonlocal techniques are known to efficiently reduce noise while preserving structures by performing the weighted averaging of similar pixels. Two pixels are considered similar if the surrounding image patches are “resembling.” Patch similarity is usually defined as the Euclidean distance between the vectors of graylevels. In this paper, a statistically grounded patch-similarity criterion suitable to SLC images is derived. A weighted maximum likelihood estimation of the SAR interferogram is then computed with weights derived in a data-driven way. Weights are defined from the intensity and interferometric phase and are iteratively refined based both on the similarity between noisy patches and on the similarity of patches from the previous estimate. The efficiency of this new interferogram construction technique is illustrated both qualitatively and quantitatively on synthetic and true data. 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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Editor-in-Chief
Antonio J. Plaza
University of Extremadura