Showing papers in "IEEE Geoscience and Remote Sensing Letters in 2008"
TL;DR: The experimental result shows that the proposed unsupervised band selection algorithms based on band similarity measurement can yield a better result in terms of information conservation and class separability than other widely used techniques.
Abstract: Band selection is a common approach to reduce the data dimensionality of hyperspectral imagery. It extracts several bands of importance in some sense by taking advantage of high spectral correlation. Driven by detection or classification accuracy, one would expect that, using a subset of original bands, the accuracy is unchanged or tolerably degraded, whereas computational burden is significantly relaxed. When the desired object information is known, this task can be achieved by finding the bands that contain the most information about these objects. When the desired object information is unknown, i.e., unsupervised band selection, the objective is to select the most distinctive and informative bands. It is expected that these bands can provide an overall satisfactory detection and classification performance. In this letter, we propose unsupervised band selection algorithms based on band similarity measurement. The experimental result shows that our approach can yield a better result in terms of information conservation and class separability than other widely used techniques.
378 citations
TL;DR: A novel method, referred to as LRTA, is proposed, which performs both spatial lower rank approximation and spectral DR, which achieves denoising reduction and DR in hyperspectral image analysis.
Abstract: In hyperspectral image (HSI) analysis, classification requires spectral dimensionality reduction (DR). While common DR methods use linear algebra, we propose a multilinear algebra method to jointly achieve denoising reduction and DR. Multilinear tools consider HSI data as a whole by processing jointly spatial and spectral ways. The lower rank-(K1, K2, K3) tensor approximation [LRTA-(K1, K2, K3)] was successfully applied to denoise multiway data such as color images. First, we demonstrate that the LRTA-(K1, K2, K3) performs well as a denoising preprocessing to improve classification results. Then, we propose a novel method, referred to as LRTAdr-(K1, K2, D3), which performs both spatial lower rank approximation and spectral DR. The classification algorithm Spectral Angle Mapper is applied to the output of the following three DR and noise reduction methods to compare their efficiency: the proposed LRTAdr-(K1, K2, D3), PCAdr, and PCAdr associated with Wiener filtering or soft shrinkage of wavelet transform coefficients.
310 citations
TL;DR: The theoretical analysis of the effects of PCA on the discrimination power of the projected subspace is presented from a general pattern classification perspective for two possible scenarios: when PCA is used as a simple dimensionality reduction tool and when it is used to recondition an ill-posed LDA formulation.
Abstract: Dimensionality reduction is a necessity in most hyperspectral imaging applications. Tradeoffs exist between unsupervised statistical methods, which are typically based on principal components analysis (PCA), and supervised ones, which are often based on Fisher's linear discriminant analysis (LDA), and proponents for each approach exist in the remote sensing community. Recently, a combined approach known as subspace LDA has been proposed, where PCA is employed to recondition ill-posed LDA formulations. The key idea behind this approach is to use a PCA transformation as a preprocessor to discard the null space of rank-deficient scatter matrices, so that LDA can be applied on this reconditioned space. Thus, in theory, the subspace LDA technique benefits from the advantages of both methods. In this letter, we present a theoretical analysis of the effects (often ill effects) of PCA on the discrimination power of the projected subspace. The theoretical analysis is presented from a general pattern classification perspective for two possible scenarios: (1) when PCA is used as a simple dimensionality reduction tool and (2) when it is used to recondition an ill-posed LDA formulation. We also provide experimental evidence of the ineffectiveness of both scenarios for hyperspectral target recognition applications.
288 citations
TL;DR: An approach for one-shot multi- class classification of multispectral data was evaluated and was more accurate than the approaches based on a series of binary classifications and had other advantages relative to the binary SVM-based approaches.
Abstract: Support vector machines (SVMs) have considerable potential for supervised classification analyses, but their binary nature has been a constraint on their use in remote sensing. This typically requires a multiclass analysis be broken down into a series of binary classifications, following either the one-against-one or one-against-all strategies. However, the binary SVM can be extended for a one-shot multiclass classification needing a single optimization operation. Here, an approach for one-shot multi- class classification of multispectral data was evaluated against approaches based on binary SVM for a set of five-class classifications. The one-shot multiclass classification was more accurate (92.00%) than the approaches based on a series of binary classifications (89.22% and 91.33%). Additionally, the one-shot multi- class SVM had other advantages relative to the binary SVM-based approaches, notably the need to be optimized only once for the parameters C and 7 as opposed to five times for one-against-all and ten times for the one-against-one approach, respectively, and used fewer support vectors, 215 as compared to 243 and 246 for the binary based approaches. Similar trends were also apparent in results of analyses of a data set of larger dimensionality. It was also apparent that the conventional one-against-all strategy could not be guaranteed to yield a complete confusion matrix that can greatly limit the assessment and later use of a classification derived by that method.
285 citations
TL;DR: The Laplacian SVM (LapSVM) is tested in the challenging problems of urban monitoring and cloud screening, in which an adequate exploitation of the wealth of unlabeled samples is critical.
Abstract: This letter presents a semisupervised method based on kernel machines and graph theory for remote sensing image classification. The support vector machine (SVM) is regularized with the unnormalized graph Laplacian, thus leading to the Laplacian SVM (LapSVM). The method is tested in the challenging problems of urban monitoring and cloud screening, in which an adequate exploitation of the wealth of unlabeled samples is critical. Results obtained using different sensors, and with low number of training samples, demonstrate the potential of the proposed LapSVM for remote sensing image classification.
262 citations
TL;DR: This letter presents the algorithm used within the MODIS production facility to produce temporally smoothed and spatially continuous biophysical data for such modeling applications and shows that the smoothed LAI agrees with high-quality MODIS LAI very well.
Abstract: Ecological and climate models require high-quality consistent biophysical parameters as inputs and validation sources. NASA's moderate resolution imaging spectroradiometer (MODIS) biophysical products provide such data and have been used to improve our understanding of climate and ecosystem changes. However, the MODIS time series contains occasional lower quality data, gaps from persistent clouds, cloud contamination, and other gaps. Many modeling efforts, such as those used in the North American Carbon Program, that use MODIS data as inputs require gap-free data. This letter presents the algorithm used within the MODIS production facility to produce temporally smoothed and spatially continuous biophysical data for such modeling applications. We demonstrate the algorithm with an example from the MODIS-leaf-area-index (LAI) product. Results show that the smoothed LAI agrees with high-quality MODIS LAI very well. Higher R-squares and better linear relationships have been observed when high-quality retrieval in each individual tile reaches 40% or more. These smoothed products show similar data quality to MODIS high-quality data and, therefore, can be substituted for low-quality retrievals or data gaps.
216 citations
TL;DR: Results indicate that it is possible to successfully downscale MERIS full resolution data to a Landsat-like spatial resolution while preserving the MERIS spectral resolution.
Abstract: An unmixing-based data fusion technique is used to generate images that have the spatial resolution of Landsat Thematic Mapper (TM) and the spectral resolution provided by the Medium Resolution Imaging Spectrometer (MERIS) sensor. The method requires the optimization of the following two parameters: the number of classes used to classify the TM image and the size of the MERIS ldquowindowrdquo (neighborhood) used to solve the unmixing equations. The ERGAS index is used to assess the quality of the fused images at the TM and MERIS spatial resolutions and to assist with the identification of the best combination of the two parameters that need to be optimized. Results indicate that it is possible to successfully downscale MERIS full resolution data to a Landsat-like spatial resolution while preserving the MERIS spectral resolution.
216 citations
TL;DR: In the first part of this letter, the use of the Induction scaling technique instead of bicubic interpolation is proposed to obtain sharper, better correlated, and hence better coregistered upscaled images.
Abstract: The fusion of multispectral (MS) and panchromatic (PAN) images is a useful technique for enhancing the spatial quality of low-resolution MS images. Liu recently proposed the smoothing-filter-based intensity modulation (SFIM) fusion technique. This technique upscales MS images using bicubic interpolation and introduces high-frequency information of the PAN image into the MS images. However, this fusion technique is plagued by blurred edges if the upscaled MS images are not accurately coregistered with the PAN image. In the first part of this letter, we propose the use of the Induction scaling technique instead of bicubic interpolation to obtain sharper, better correlated, and hence better coregistered upscaled images. In the second part, we propose a new fusion technique derived from induction, which is named ldquoIndusion.rdquo In this method, the high-frequency content of the PAN image is extracted using a pair of upscaling and downscaling filters. It is then added to an upscaled MS image. Finally, a comparison of SFIM (with both bicubic interpolation and induction scaling) is presented along with the fusion results obtained by IHS, discrete wavelet transform, and the proposed Indusion techniques using Quickbird satellite images.
202 citations
TL;DR: This letter extends the analysis to 3D imaging via delay-and-sum beamforming in the presence of a single uniform wall to provide valuable information on target heights that can be used for enhancing target discrimination/identification.
Abstract: Through-the-wall imaging and urban sensing is an emerging area of research and development. The incorporation of the effects of signal propagation through wall material in producing an indoor image is important for reliable through-the-wall mission operations. We have previously analyzed wall effects, such as refraction and change in propagation speed, and designed a wideband beamformer for 2D imaging using line arrays. In this letter, we extend the analysis to 3D imaging via delay-and-sum beamforming in the presence of a single uniform wall. The third dimension provides valuable information on target heights that can be used for enhancing target discrimination/identification. Supporting simulation results are provided.
195 citations
TL;DR: An implementation in ITT Visual Information Solutions IDL of a new algorithm that automates the calculation of river widths using raster-based classifications of inundation extent derived from remotely sensed imagery that is comparable in quality to measurements derived using manual techniques.
Abstract: RivWidth is an implementation in ITT Visual Information Solutions IDL of a new algorithm that automates the calculation of river widths using raster-based classifications of inundation extent derived from remotely sensed imagery. The algorithm utilizes techniques of boundary definition to extract a river centerline, derives a line segment that is orthogonal to this line at each centerline pixel, and then computes the total river width along each orthogonal. The output of RivWidth is comparable in quality to measurements derived using manual techniques; yet, it continuously generates thousands of width values along an entire stream course, even in multichannel river systems. Uncertainty in RivWidth principally depends on the quality of the water classification used as an input, though pixel resolution and the values of input parameters play lesser roles. Source code for RivWidth can be obtained by visiting http://pavelsky.googlepages.com/rivwidth.
179 citations
TL;DR: Analytical tools and examples of computing confidence intervals and regions around these estimates commonly presented as points on receiver operating characteristic (ROC) curves are provided.
Abstract: Many researchers have presented results showing the empirical performance of target detection algorithms using hyperspectral or synthetic aperture radar imagery. In nearly all cases, these probabilities of detection and false alarm are presented as precise values as opposed to their true nature as estimates of random values. In this letter, we provide analytical tools and examples of computing confidence intervals and regions around these estimates commonly presented as points on receiver operating characteristic (ROC) curves. It is suggested that these tools be adopted by researchers when presenting their results to provide their audience with a quantitative metric for proper interpretation of empirically estimated ROC curves.
TL;DR: A novel classification method, taking regions as elements, is proposed using a Markov random field (MRF), using a Wishart-based maximum likelihood, based on regions, to obtain a classification map.
Abstract: The scattering measurements of individual pixels in polarimetric SAR images are affected by speckle; hence, the performance of classification approaches, taking individual pixels as elements, would be damaged. By introducing the spatial relation between adjacent pixels, a novel classification method, taking regions as elements, is proposed using a Markov random field (MRF). In this method, an image is oversegmented into a large amount of rectangular regions first. Then, to use fully the statistical a priori knowledge of the data and the spatial relation of neighboring pixels, a Wishart MRF model, combining the Wishart distribution with the MRF, is proposed, and an iterative conditional mode algorithm is adopted to adjust oversegmentation results so that the shapes of all regions match the ground truth better. Finally, a Wishart-based maximum likelihood, based on regions, is used to obtain a classification map. Real polarimetric images are used in experiments. Compared with the other three frequently used methods, higher accuracy is observed, and classification maps are in better agreement with the initial ground maps, using the proposed method.
TL;DR: This letter presents a modification to the established Fraunhofer line discrimination method for improving the accuracy of the solar-induced chlorophyll fluorescence retrieval over terrestrial vegetation by introducing two correction coefficients that relate the values of the fluorescence and the reflectance inside and outside the absorption band.
Abstract: This letter presents a modification to the established Fraunhofer line discrimination (FLD) method for improving the accuracy of the solar-induced chlorophyll fluorescence (ChF) retrieval over terrestrial vegetation. The FLD method relies on the decoupling of reflected and ChF emitted radiation by the evaluation of measurements inside and outside the absorption bands. The improved FLD method introduces two correction coefficients that relate the values of the fluorescence and the reflectance inside and outside the absorption band. The new method uses the full spectral information around the absorption band to derive these coefficients. A sensitivity analysis has been performed to evaluate the impact of the correction coefficients on the accuracy of the ChF estimation. The new formulation has been tested for the O2 A-band on synthetic data obtaining lower errors in comparison to the standard FLD and has been successfully applied to real measurements at canopy level.
TL;DR: Simulations show that by combining the DKT and the Doppler phase compensation methods, the moving target can be well imaged in high signal-clutter-ratio case.
Abstract: In this letter, a synthetic aperture radar (SAR) data reformatting approach named Doppler keystone transform (DKT) is proposed to correct the range migration of a moving target. By using the DKT, the SAR imaging program, i.e., the 2-D matched filtering, can be transformed into separate 1-D operations along range or azimuth direction, and therefore, the DKT is suitable for the parallel implementation of SAR imaging of the moving target. Our simulations show that by combining the DKT and the Doppler phase compensation methods, the moving target can be well imaged in high signal-clutter-ratio case.
TL;DR: The KummerU-based distribution should provide in many cases a better representation of textured areas than the classic K distribution, and it is shown that the new model can discriminate regions with different texture distribution in a segmentation experiment with synthetic textured PolSAR images.
Abstract: The multilook polarimetric synthetic aperture radar (PolSAR) covariance matrix is generally modeled by a complex Wishart distribution. For textured areas, the product model is used, and the texture component is modeled by a Gamma distribution. In many cases, the assumption of Gamma-distributed texture is not appropriate. The Fisher distribution does not have this limitation and can represent a large set of texture distributions. As an example, we examine its advantage for an urban area. From a Fisher-distributed texture component, we derive the distribution of the complex covariance matrix for multilook PolSAR data. The obtained distribution is expressed in terms of the KummerU confluent hypergeometric function of the second kind. Those distributions are related to the Mellin transform and second-kind statistics (Log-statistics). The new KummerU-based distribution should provide in many cases a better representation of textured areas than the classic K distribution. Finally, we show that the new model can discriminate regions with different texture distribution in a segmentation experiment with synthetic textured PolSAR images.
TL;DR: An innovative constant false alarm rate (CFAR) algorithm was studied for ship detection using synthetic aperture radar (SAR) images of the sea and detected the most number of ships with the smallest number of false alarms.
Abstract: An innovative constant false alarm rate (CFAR) algorithm was studied for ship detection using synthetic aperture radar (SAR) images of the sea. Two advances were achieved. An alpha-stable distribution rather than a traditional Weibull or -distribution was used to model the distribution of sea clutter. The distribution of sea clutter in a SAR image was typically heterogeneous, caused mainly by variable wind and current conditions. Image segmentation was carried out to improve the homogeneity of the distribution in each subimage or region. In comparison with ship detection using the CFAR algorithms based on the Weibull or K -distribution, our algorithm detected the most number of ships with the smallest number of false alarms.
TL;DR: The 2007 data fusion contest was dealing with the extraction of a land use/land cover maps in and around an urban area, exploiting multitemporal and multisource coarse-resolution data sets, and the best algorithm is based on a neural classification enhanced by preprocessing and postprocessing steps.
Abstract: The 2007 data fusion contest that was organized by the IEEE Geoscience and Remote Sensing Data Fusion Technical Committee was dealing with the extraction of a land use/land cover maps in and around an urban area, exploiting multitemporal and multisource coarse-resolution data sets. In particular, synthetic aperture radar and optical data from satellite sensors were considered. Excellent indicators for mapping accuracy were obtained by the top teams. The best algorithm is based on a neural classification enhanced by preprocessing and postprocessing steps.
TL;DR: An unsupervised technique for change detection (CD) in very high geometrical resolution images is proposed, which is based on the use of morphological filters and increases the accuracy of the CD process as compared with the standard CVA approach.
Abstract: An unsupervised technique for change detection (CD) in very high geometrical resolution images is proposed, which is based on the use of morphological filters. This technique integrates the nonlinear and adaptive properties of the morphological filters with a change vector analysis (CVA) procedure. Different morphological operators are analyzed and compared with respect to the CD problem. Alternating sequential filters by reconstruction proved to be the most effective, permitting the preservation of the geometrical information of the structures in the scene while filtering the homogeneous areas. Experimental results confirm the effectiveness of the proposed technique. It increases the accuracy of the CD process as compared with the standard CVA approach.
TL;DR: In this letter, a radar combining Doppler processing and spatial beamforming is presented for tracking humans through walls, and the CLEAN and RELAX algorithms are implemented and their performances are compared with standard beamforming.
Abstract: In this letter, a radar combining Doppler processing and spatial beamforming is presented for tracking humans through walls. Multiple targets are tracked by resolving the targets in the Doppler and bearing space. To overcome the high sidelobes associated with an array of limited size, the CLEAN and RELAX algorithms are implemented, and their performances are compared with standard beamforming. The radar is tested in indoor line-of-sight and through-wall scenarios for multiple loudspeakers and human subjects.
TL;DR: The quantitative assessment of segmentation accuracy based on reference objects is derived from an aerial image, and a high-resolution synthetic aperture radar scene shows an improvement of 20%-40% in object accuracy by applying the proposed procedure.
Abstract: This letter proposes an optimization approach that enhances the quality of image segmentation using the software Definiens Developer. The procedure aims at the minimization of over- and undersegmentations in order to attain more accurate segmentation results. The optimization iteratively combines a sequence of multiscale segmentation, feature-based classification, and classification-based object refinement. The developed method has been applied to various remotely sensed data and is compared to the results achieved with the established segmentation procedures provided by the Definiens Developer software. The quantitative assessment of segmentation accuracy based on reference objects is derived from an aerial image, and a high-resolution synthetic aperture radar scene shows an improvement of 20%-40% in object accuracy by applying the proposed procedure.
TL;DR: This letter designs a bistatic configuration with a stationary transmitter and a forward-looking airborne receiver and shows its imaging characteristics, and the simulation results are exhibited, which validate the correctness of the analysis and prove the 2-D imaging ability offorward-looking bistatics SAR.
Abstract: Forward-looking imaging has many potential applications, but it is impossible with the usual monostatic synthetic aperture radar (SAR) principle. Through the bistatic SAR configuration, forward-looking imaging can be realized for one of the bistatic platforms. This letter designs a bistatic configuration with a stationary transmitter and a forward-looking airborne receiver. It then analyzes the 2-D resolution and finds out which geometric parameter affects the imaging ability mostly. Besides, it gives out the signal formulation in the frequency domain and shows its imaging characteristics. Then, an imaging method is chosen for this special configuration, and the simulation results are exhibited, which validate the correctness of the analysis and prove the 2-D imaging ability of forward-looking bistatic SAR.
TL;DR: This letter provides a complete set of split-window coefficients that can be used to retrieve land surface temperature from thermal infrared sensors onboard the most popular remote-sensing satellites, facilitating and homogenizing the task of retrieving this parameter from different common sensors.
Abstract: In this letter, we provide a complete set of split-window coefficients that can be used to retrieve land surface temperature (LST) from thermal infrared sensors onboard the most popular remote-sensing satellites: ERS-ATSR2, ENVISAT-AATSR, Terra/Aqua-MODIS, NOAA series-AVHRR, METOP-AVHRR3, GOES series-IMAGER, and MSG1/MSG2-SEVIRI. The coefficients have been obtained by minimization from an extensive simulated database constructed from MODTRAN radiative transfer code calculations, emissivity spectra extracted from spectral libraries, and spectral response functions of the thermal bands considered. This letter also analyzes the magnitude of the error on the LST retrieval and the contribution to the error of the different uncertainties. Results are summarized in a lookup table useful for scientists interested on land surface retrievals at global scale, thereby facilitating and homogenizing the task of retrieving this parameter from different common sensors.
TL;DR: The results showed an improvement from 3% to 20%.
Abstract: We propose to fuse the high spatial content of two 250-m spectral bands of the moderate resolution imaging spectroradiometer (MODIS) into its five 500-m bands using wavelet-based multiresolution analysis. Our objective was to test the effectiveness of this technique to increase the accuracy of snow mapping in mountainous environments. To assess the performance of this approach, we took advantage of the simultaneity between the advanced spaceborne thermal emission and reflection radiometer (ASTER) and MODIS sensors. With a 15-m spatial resolution, the ASTER sensor provided reference snow maps, which were then compared to MODIS-derived snow maps. The benefit of the method was assessed through the investigation of various metrics, which showed an improvement from 3% to 20%. Therefore, the enhanced snow map is of great benefit for environmental and hydrological applications in steep terrain.
TL;DR: This letter analyzes different approaches for polarimetric optimization of multibaseline (MB) interferometric coherences and concludes that MB coherence optimization does improve the accuracy in the estimation of dominant SMs and the associatedinterferometric phases.
Abstract: This letter analyzes different approaches for polarimetric optimization of multibaseline (MB) interferometric coherences. Two general methods are developed to simultaneously optimize coherences for more than two data sets. The first method provides every data set with a distinct dominant scattering mechanism (SM). The second optimization method is constrained to use equal SMs at all data sets. As the experimental results indicate, MB coherence optimization does improve the accuracy in the estimation of dominant SMs and the associated interferometric phases. Both methods are evaluated on real data acquired by the German Aerospace Agency (DLR)'s enhanced synthetic aperture radar sensor (ESAR) at L-band.
TL;DR: A new technique for daytime GPP estimation in maize is presented based on the close and consistent relationship between GPP and crop chlorophyll content, and entirely on remotely sensed data, which opens new possibilities for analyzing the spatiotemporal variation of the GPP of crops using the extensive archive of Landsat imagery acquired since the early 1980s.
Abstract: There is a growing interest in monitoring the gross primary productivity (GPP) of crops due mostly to their carbon sequestration potential. Both within- and between-field variability are important components of crop GPP monitoring, particularly for the estimation of carbon budgets. In this letter, we present a new technique for daytime GPP estimation in maize based on the close and consistent relationship between GPP and crop chlorophyll content, and entirely on remotely sensed data. A recently proposed chlorophyll index (CI), which involves green and near-infrared spectral bands, was used to retrieve daytime GPP from Landsat Enhanced Thematic Mapper Plus (ETM+) data. Because of its high spatial resolution (i.e., 30 30 m/pixel), this satellite system is particularly appropriate for detecting not only between- but also within-field GPP variability during the growing season. The CI obtained using atmospherically corrected Landsat ETM+ data was found to be linearly related with daytime maize GPP: root mean squared error of less than 1.58 in a GPP range of 1.88 to 23.1 ; therefore, it constitutes an accurate surrogate measure for GPP estimation. For comparison purposes, other vegetation indices were also tested. These results open new possibilities for analyzing the spatiotemporal variation of the GPP of crops using the extensive archive of Landsat imagery acquired since the early 1980s.
TL;DR: A multiple-component scattering model (MCSM) is proposed to decompose polarimetric synthetic aperture radar (PolSAR) images and it can be found that double-bounce, helix, and wire scattering are predominant in urban areas.
Abstract: A multiple-component scattering model (MCSM) is proposed to decompose polarimetric synthetic aperture radar (PolSAR) images. The MCSM extends a three-component scattering model, which describes single-bounce, double-bounce, volume, helix, and wire scattering as elementary scattering mechanisms in the analysis of PolSAR images. It can be found that double-bounce, helix, and wire scattering are predominant in urban areas. These elementary scattering mechanisms correspond to the asymmetric reflection condition that the copolar and cross-polar correlations are not close to zero. The MCSM is demonstrated with a German Aerospace Center (DLR) Experimental Synthetic Aperture Radar (ESAR) L-band full-polarized image of the Oberpfaffenhofen Test Site Area (DE), Germany, which was obtained on September 30, 2000. The result of this decomposition confirmed that the proposed model is effective for analysis of buildings in urban areas.
TL;DR: A simple and very effective filtering technique is proposed for synthetic aperture radar (SAR) sea oil slick observation, showing the effectiveness of the proposed model and the capabilities of the filter to both observe oil slicks and distinguish them from biogenic look-alikes.
Abstract: In this letter, a fully polarimetric electromagnetic model for sea surface Mueller matrix is exploited to characterize the scattering from oil and biogenic slicks, under low-to-moderate wind conditions. The model predicts a completely different scattering mechanism for oil-covered and oil-free sea surface, while biogenic slicks are indistinguishable from sea surface in terms of polarimetric scattering. Following this theoretical rationale, a simple and very effective filtering technique is proposed for synthetic aperture radar (SAR) sea oil slick observation. Experiments, accomplished over C-band multilook complex SIR-C/X-SAR data, show the effectiveness of the proposed model and the capabilities of the filter to both observe oil slicks and distinguish them from biogenic look-alikes.
TL;DR: A new numerical procedure to get superresolution microwave scanning radiometer measurements is presented, and its solution is pursued by means of a superresolution numerical procedure based on the Tikhonov regularization method.
Abstract: Microwave radiometer measurements are exploited to extract important geophysical information. Although it is beneficial to merge different frequency channels, it requires extra effort to refer all measurements to a common spatial resolution. Therefore, the capability to enhance the spatial resolution of a single channel is of special interest. In this study, a new numerical procedure to get superresolution microwave scanning radiometer measurements is presented. The approach is physically based on the occurrence of multiple partially correlated measurements. Mathematically, the approach is equivalent to a linear inversion problem, and its solution is pursued by means of a superresolution numerical procedure based on the Tikhonov regularization method. A set of numerical examples illustrates the results of the study in which hypothetical scanning microwave radiometer sensor configuration and reference test cases have been considered.
TL;DR: This letter presents a simultaneous band selection and endmember detection algorithm for hyperspectral imagery and shows the ability to find the correct endmembers and abundance values and strong classification accuracies.
Abstract: This letter presents a simultaneous band selection and endmember detection algorithm for hyperspectral imagery. This algorithm is an extension of the sparsity promoting iterated constrained endmember (SPICE) algorithm. The extension adds spectral band weights and a sparsity promoting prior to the SPICE objective function to provide integrated band selection. In addition to solving for endmembers, the number of endmembers, and end- member fractional maps, this algorithm attempts to autonomously perform band selection and to determine the number of spectral bands required for a particular scene. Results are presented on a simulated data set and the AVIRIS Indian Pines data set. Experiments on the simulated data set show the ability to find the correct endmembers and abundance values. Experiments on the Indian Pines data set show strong classification accuracies in comparison to previously published results.
TL;DR: A coherence-based technique for atmospheric artifact removal in ground-based (GB) zero-baseline synthetic aperture radar (SAR) acquisitions and the need to compensate for the resulting phase-difference errors when retrieving interferometric information is put forward.
Abstract: In this letter, a coherence-based technique for atmospheric artifact removal in ground-based (GB) zero-baseline synthetic aperture radar (SAR) acquisitions is proposed. For this purpose, polarimetric measurements acquired using the GB-SAR sensor developed at the Universitat Politecnica de Catalunya are employed. The heterogeneous environment of Collserola Park in the outskirts of Barcelona, Spain, was selected as the test area. Data sets were acquired at X-band during one week in June 2005. The effects of the atmosphere variations between successive zero-baseline SAR polarimetric acquisitions are treated here in detail. The need to compensate for the resulting phase-difference errors when retrieving interferometric information is put forward. A compensation technique is then proposed and evaluated using the control points placed inside the observed scene.