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Geoscience and Remote Sensing, IEEE Transactions on

Issue 7 • Date July 2010

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Displaying Results 1 - 25 of 32
  • [Front cover]

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

    Page(s): C2
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  • Table of contents

    Page(s): 2769 - 2770
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  • A Method of Obtaining Ice Concentration and Floe Size From Shipboard Oblique Sea Ice Images

    Page(s): 2771 - 2780
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (846 KB) |  | HTML iconHTML  

    Digital cameras are widely used on research vessels to ascertain ice conditions in ice-infested seas. However, geometric distortion of images cannot be avoided if the camera is tilted relative to the sea surface. In this study, the geometric distortion induced by oblique photography was quantitatively estimated based on photogrammetric theory to enable accurate measurement of sea ice features. One accurate method of calculating the ice concentration and floe size was developed, and a corresponding simplified method was also tested. Proper selection of camera system parameters (camera height, tilt, and focal length) cannot only avoid possible errors induced by geometric distortion but also make full use of the image information. Additionally, oblique sea ice images obtained during the 2006 Winter Weddell Outflow Study were investigated using the methods developed here. The results revealed that the geometric distortion of oblique images could be accurately corrected by analyzing each image pixel and that the simplified method could be applied to ensure the precision and efficiency of calculating ice features. View full abstract»

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  • Multiple-Layer Adaptation of HUT Snow Emission Model: Comparison With Experimental Data

    Page(s): 2781 - 2794
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (855 KB) |  | HTML iconHTML  

    Modeling of snow emission at microwave frequencies is necessary in order to understand the complex relations between the emitted brightness temperature and snowpack characteristics such as density, grain size, moisture content, and vertical structure. Several empirical, semiempirical, and purely theoretical models for the prediction of snow emission properties have been developed in recent years. In this paper, we investigate the capability of one such model to simulate snow emission during the peak snow season-a new multilayer version of the Helsinki University of Technology (HUT) snow model. Developed with a single layer, the original HUT model was easily applied over large geographic areas for the estimation of snow cover characteristics by model inversion. A single homogenous layer, however, may not accurately allow the simulation of vertically structured natural snowpacks. The new modification to the model allows the simulation of emission from a snowpack with several snow or ice layers, with the individual component layers treated as in the original HUT model. The results of modeled snowpack emission, using both the original model and the new multilayer modification, are compared with reference measurements made using ground-based radiometers deployed in Finland and Canada. Detailed in situ measurements of the snowpack are used to set the model inputs. We show that, in most cases, use of the multiple-layer model improves estimates for the higher frequencies tested, with up to 38% improvement in rms error. In some cases, however, the use of the multiple-layer model weakens model performance particularly at lower frequencies. View full abstract»

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  • Vehicle Detection in Very High Resolution Satellite Images of City Areas

    Page(s): 2795 - 2806
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1314 KB) |  | HTML iconHTML  

    Current traffic research is mostly based on data from fixed-installed sensors like induction loops, bridge sensors, and cameras. Thereby, the traffic flow on main roads can partially be acquired, while data from the major part of the entire road network are not available. Today's optical sensor systems on satellites provide large-area images with 1-m resolution and better, which can deliver complement information to traditional acquired data. In this paper, we present an approach for automatic vehicle detection from optical satellite images. Therefore, hypotheses for single vehicles are generated using adaptive boosting in combination with Haar-like features. Additionally, vehicle queues are detected using a line extraction technique since grouped vehicles are merged to either dark or bright ribbons. Utilizing robust parameter estimation, single vehicles are determined within those vehicle queues. The combination of implicit modeling and the use of a priori knowledge of typical vehicle constellation leads to an enhanced overall completeness compared to approaches which are only based on statistical classification techniques. Thus, a detection rate of over 80% is possible with very high reliability. Furthermore, an approach for movement estimation of the detected vehicle is described, which allows the distinction of moving and stationary traffic. Thus, even an estimate for vehicles' speed is possible, which gives additional information about the traffic condition at image acquisition time. View full abstract»

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  • Road Extraction From Satellite Images Using Particle Filtering and Extended Kalman Filtering

    Page(s): 2807 - 2817
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (696 KB) |  | HTML iconHTML  

    Extended Kalman filter (EKF) has previously been employed to extract road maps in satellite images. This filter traces a single road until a stopping criterion is satisfied. In our new approach, we have combined EKF with a special particle filter (PF) in order to regain the trace of the road beyond obstacles, as well as to find and follow different road branches after reaching to a road junction. In this approach, first, EKF traces a road until a stopping criterion is met. Then, instead of terminating the process, the results are passed to the PF algorithm which tries to find the continuation of the road after a possible obstacle or to identify all possible road branches that might exist on the other side of a road junction. For further improvement, we have modified the procedure for obtaining the measurements by decoupling this process from the current state prediction of the filter. Removing the dependence of the measurement data to the predicted state reduces the potential for instability of the road-tracing algorithm. Furthermore, we have constructed a method for dynamic clustering of the road profiles in order to maintain tracking when the road profile undergoes some variations due to changes in the road width and intensity. View full abstract»

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  • A Novel Texture-Preceded Segmentation Algorithm for High-Resolution Imagery

    Page(s): 2818 - 2828
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1223 KB) |  | HTML iconHTML  

    Image segmentation is crucial to object-oriented remote sensing imagery analysis. In this paper, a novel texture-preceded segmentation algorithm is proposed for high-resolution remote sensing imagery, in which texture clustering is first carried out as a loose constraint for later segmentation. The algorithm is based on the graph models of region adjacency graph and nearest neighbor graph, which can achieve fast node merging, depending on the global optimum. Here, a combined distance, composed of texture, spectral, and shape features, is established to measure the similarity between nodes and gives the same semantic descriptions for the texture objects. Then, the combined distance is applied to graph models, and the final segmentation result can be obtained iteratively by fast merging. During the merging process, optimal sequence merging interacts with texture clustering to refine the real edges of a texture region. This algorithm cannot only merge the homogeneous texture segments with spectral variability easily but can also detect the real object boundaries well. The experiments on high-resolution imagery show that, in terms of the same number of segments, the proposed algorithm can improve segmentation accuracy by 10%-20% compared to the results obtained by pure spectral features with Definiens Developer software. View full abstract»

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  • Fully Automatic Subpixel Image Registration of Multiangle CHRIS/Proba Data

    Page(s): 2829 - 2839
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1246 KB) |  | HTML iconHTML  

    Subpixel image registration is the key to successful image fusion and superresolution enhancement of multiangle satellite data. Multiangle image registration poses two main challenges: 1) Images captured at large view angles are susceptible to resolution change and blurring, and 2) local geometric distortion caused by topographic effects and/or platform instability may be important. In this paper, we propose a two-step nonrigid automatic registration scheme for multiangle satellite images. In the first step, control points (CPs) are selected in a preregistration process based on the scale-invariant feature transform (SIFT). However, the number of CPs obtained in this first step may be too few and/or CPs may be unevenly distributed. To remediate these problems, in a second step, the preliminary registered image is subdivided into chips of 64 × 64 pixels, and each chip is matched with a corresponding chip in the reference image using normalized cross correlation (NCC). By doing so, more CPs with better spatial distribution are obtained. Two criteria are applied during the generation of CPs to identify outliers. Selected SIFT and NCC CPs are used for defining a nonrigid thin-plate-spline model. The proposed registration scheme has been tested using data from the Compact High Resolution Imaging Spectrometer (CHRIS) onboard the Project for On-Board Autonomy (Proba) satellite. Experimental results demonstrate that the proposed method works well in areas with little variation in topography. Application in areas with more pronounced relief would require the use of orthorectified image data in order to achieve subpixel registration accuracy. View full abstract»

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  • A Dynamic Subspace Method for Hyperspectral Image Classification

    Page(s): 2840 - 2853
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2706 KB) |  | HTML iconHTML  

    Many studies have demonstrated that multiple classifier systems, such as the random subspace method (RSM), obtain more outstanding and robust results than a single classifier on extensive pattern recognition issues. In this paper, we propose a novel subspace selection mechanism, named the dynamic subspace method (DSM), to improve RSM on automatically determining dimensionality and selecting component dimensions for diverse subspaces. Two importance distributions are proposed to impose on the process of constructing ensemble classifiers. One is the distribution of subspace dimensionality, and the other is the distribution of band weights. Based on the two distributions, DSM becomes an automatic, dynamic, and adaptive ensemble. The real data experimental results show that the proposed DSM obtains sound performances than RSM, and that the classification maps remarkably produce fewer speckles. View full abstract»

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  • Cost and Scalability Improvements to the Karhunen–Loêve Transform for Remote-Sensing Image Coding

    Page(s): 2854 - 2863
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1884 KB) |  | HTML iconHTML  

    The Karhunen-Loêve transform (KLT) is widely used in hyperspectral image compression because of its high spectral decorrelation properties. However, its use entails a very high computational cost. To overcome this computational cost and to increase its scalability, in this paper, we introduce a multilevel clustering approach for the KLT. As the set of different multilevel clustering structures is very large, a two-stage process is used to carefully pick the best members for each specific situation. First, several candidate structures are generated through local search and eigenthresholding methods, and then, candidates are further screened to select the best clustering configuration. Two multilevel clustering combinations are proposed for hyperspectral image compression: one with the coding performance of the KLT but with much lower computational requirements and increased scalability and another one that outperforms a lossy wavelet transform, as spectral decorrelator, in quality, cost, and scalability. Extensive experimental validation is performed, with images from both the AVIRIS and Hyperion sets, and with JPEG2000, 3D-TCE, and CCSDS-Image Data Compression recommendation as image coders. Experiments also include classification-based results produced by k-means clustering and Reed-Xiaoli anomaly detection. View full abstract»

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  • Automatic Extraction of Control Points for the Registration of Optical Satellite and LiDAR Images

    Page(s): 2864 - 2879
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2554 KB) |  | HTML iconHTML  

    A novel method for automatic extraction of control points for the registration of optical images with Light Detection And Ranging (LiDAR) data is proposed. It is based on transformation-invariant detection of salient image disks (SIDs), which determine the location of control points as the centers of the corresponding image fragments. The SID is described by a feature vector, which, in addition to the coordinates and diameter, includes intensity descriptors and region shape characteristics of the image fragment. SIDs are effectively extracted using multiscale isotropic matched filtering-a visual attention operator that indicates image locations with high-intensity contrast, homogeneity, and local shape saliency. This paper discusses the extraction of control points from both natural landscapes and structured scenes with man-made objects. Registration experiments conducted on QuickBird imagery with corresponding LiDAR data validated the proposed approach. View full abstract»

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  • Sensitivity of Support Vector Machines to Random Feature Selection in Classification of Hyperspectral Data

    Page(s): 2880 - 2889
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1692 KB) |  | HTML iconHTML  

    The accuracy of supervised land cover classifications depends on factors such as the chosen classification algorithm, adequate training data, the input data characteristics, and the selection of features. Hyperspectral imaging provides more detailed spectral and spatial information on the land cover than other remote sensing resources. Over the past ten years, traditional and formerly widely accepted statistical classification methods have been superseded by more recent machine learning algorithms, e.g., support vector machines (SVMs), or by multiple classifier systems (MCS). This can be explained by limitations of statistical approaches with regard to high-dimensional data, multimodal classes, and often limited availability of training data. In the presented study, MCSs based on SVM and random feature selection (RFS) are applied to explore the potential of a synergetic use of the two concepts. We investigated how the number of selected features and the size of the MCS influence classification accuracy using two hyperspectral data sets, from different environmental settings. In addition, experiments were conducted with a varying number of training samples. Accuracies are compared with regular SVM and random forests. Experimental results clearly demonstrate that the generation of an SVM-based classifier system with RFS significantly improves overall classification accuracy as well as producer's and user's accuracies. In addition, the ensemble strategy results in smoother, i.e., more realistic, classification maps than those from stand-alone SVM. Findings from the experiments were successfully transferred onto an additional hyperspectral data set. View full abstract»

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  • Eigenmethod for Feature Matching of Pre- and Postevent Images Exploiting Adjacency

    Page(s): 2890 - 2898
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (782 KB) |  | HTML iconHTML  

    With the continuing increase in the number of images collected everyday from different sensors, the automated registration of multisensor/multispectral images has become a very important issue. This is particularly true when pre- and postevent image comparison is concerned: For this particular application, the requirement of obtaining the earliest possible postevent image imposes the use of data potentially possessing significantly different characteristics with respect to the pre-event image. Strongly inhomogeneous image pairs require robust automatic registration techniques, preferably based on resolution-independent feature-based registration. In a previous paper, we proposed a mode-based feature-matching scheme mutated from the computer vision domain and adapted to pre- and postevent feature matching. Some of the weak points highlighted in that first version are addressed in this paper, where a new version of the method is proposed, which exploits a new piece of information, i.e., the adjacency between feature points, generally preserved across the disaster event. Extensive generation of synthetic cases allowed one to obtain significant feedback and, consequently, tune the algorithm. Three real cases of pre- and postevent feature matching on high-resolution satellite images are shown and discussed. View full abstract»

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  • Performance Study of a Cross-Frequency Detection Algorithm for Pulsed Sinusoidal RFI in Microwave Radiometry

    Page(s): 2899 - 2908
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    An analysis of the performance of a cross-frequency detection algorithm for pulsed sinusoidal radio frequency interference (RFI) is performed. The performance obtained is compared with that of pulse and kurtosis detection methods that have been previously reported. The results of the study show that the cross-frequency algorithm provides good performance in detecting pulsed sinusoidal RFI at high duty cycles, including the case of continuous sinusoidal interference. The use of the cross-frequency algorithm requires choice of a detection threshold that in practice can be estimated using measured data. The effect of this threshold estimation on the detector performance is also examined. View full abstract»

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  • Improved Hurricane Ocean Vector Winds Using SeaWinds Active/Passive Retrievals

    Page(s): 2909 - 2923
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3353 KB) |  | HTML iconHTML  

    The SeaWinds scatterometer, onboard the QuikSCAT satellite, infers global ocean vector winds (OVWs); however, for a number of reasons, these measurements in hurricanes are significantly degraded. This paper presents an improved hurricane OVW retrieval approach, known as Q-Winds, which is derived from combined SeaWinds active and passive measurements. In this technique, the effects of rain are implicitly included in a new geophysical model function, which relates oceanic brightness temperature and radar backscatter measurements (at the top of the atmosphere) to the surface wind vector under both clear sky and in the presence of light to moderate rain. This approach extends the useful wind speed measurement range for tropical cyclones beyond that exhibited by the standard SeaWinds Project Level-2B (L2B) 12.5-km wind vector algorithm. A description of the Q-Winds algorithm is given, and examples of OVW retrievals are presented for the Q-Winds and L2B 12.5-km algorithms for ten hurricane overpasses in 2003-2008. These data are also compared to independent surface wind vector estimates from the National Oceanic and Atmospheric Administration Hurricane Research Division's objective hurricane surface wind analysis technique known as H*Wind. These comparisons suggest that the Q-Winds OVW product agrees better with independently derived H^ Wind analysis winds than does the conventional L2B OVW product. View full abstract»

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  • Radar Imaging From Geosynchronous Orbit: Temporal Decorrelation Aspects

    Page(s): 2924 - 2929
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (242 KB) |  | HTML iconHTML  

    Synthetic aperture radar imaging from geosynchronous orbit has significant potential advantages over conventional low-Earth orbit radars, but it also has challenges to overcome. The baseline mission we consider is an L-band geosynchronous passive (bistatic) radar achieving a spatial resolution of about 100 m with an integration time of 8 h. The atmosphere changes its structure on timescales of minutes to hours, and this has to be compensated if useful images are to be provided. The analysis shows that ionospheric delay is the major source of temporal decorrelation; other effects, such as tropospheric delay and Earth tides, have to be dealt with but appear to be easier to handle. View full abstract»

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  • Optimal Waveform Design for Improved Indoor Target Detection in Sensing Through-the-Wall Applications

    Page(s): 2930 - 2941
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1648 KB) |  | HTML iconHTML  

    This paper deals with waveform design for improved detection and classification of targets behind walls and enclosed structures. The target impulse response is incorporated in an optimum design of the transmitted waveform which aims at maximizing the signal-to-interference and noise ratio (SINR) at the receiver output. The interference represents signal-dependent clutter which, along with the wall, degrades the receiver performance compared to the free-space and zero-clutter case. Computer simulations show sensitivity of the optimum waveform to target orientation but depict an SINR enhancement over chirped waveform radar emissions at all aspect angles. Numerical electromagnetic modeling is used to provide the impulse response of typical indoor stationary targets, namely, tables, chairs, and humans. View full abstract»

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  • Decorrelation of L-Band and C-Band Interferometry Over Vegetated Areas in California

    Page(s): 2942 - 2952
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2266 KB) |  | HTML iconHTML  

    Temporal decorrelation is one of the main limitations for recovering interseismic deformation along the San Andreas Fault system using interferometric synthetic aperture radar. To assess the improved correlation properties of L-band with respect to C-band, we analyzed L-band Advanced Land Observation Satellite (ALOS) interferograms with a range of temporal and spatial baselines over three vegetated areas in California and compared them with corresponding C-band European Remote Sensing Satellite (ERS) interferograms. Over the highly vegetated Northern California forests in the Coast Range area, ALOS remains remarkably well correlated over a 2-year period, whereas an ERS interferogram with a similar temporal and spatial baseline lost correlation. In Central California near Parkfield, we found a similar pattern in decorrelation behavior, which enabled the recovery of a fault creep and a local uplifting signal at L-band that was not apparent at C-band. In the Imperial Valley in Southern California, both ALOS and ERS have low correlation over farmlands. ALOS has lower correlation over some sandy surfaces than ERS, probably due to low signal-to-noise ratio. In general, L-band interferograms with similar seasonal acquisitions have higher correlation than those with dissimilar season. For both L- and C-band, correlation over vegetated areas decreases with time for intervals less than 1 year and then remains relatively constant at longer time intervals. The decorrelation time for L-band is more than 2 years in the forest in California whereas that for C-band is less than 6 months. Overall, these results suggest that L-band interferograms will reveal near-fault interseismic deformation once sufficient data become available. View full abstract»

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  • On the Role of Phase Stability in SAR Multibaseline Applications

    Page(s): 2953 - 2966
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (695 KB) |  | HTML iconHTML  

    This paper is meant to present a statistical analysis of the role of propagation disturbances (PDs), such as those due to atmospheric disturbances or to residual platform motion, in multibaseline synthetic aperture radar (SAR) interferometry (InSAR) and tomography (T-SAR) applications. The analysis will consider both pointlike and distributed targets in such a way as to cover all the cases that are relevant in the applications. In order to provide a tool for the evaluation of the impact of PDs on the analysis of an arbitrary scenario, a definition of signal-to-noise ratio (SNR) will be introduced that accounts for both the presence of PDs and the characteristics of the imaged scene. In the case of pointlike targets, it will be shown that such definition of SNR allows reusing well known results following after the Neyman-Pearson theory, thus providing a straightforward tool to asses phase-stability requirements for the detection and localization of multiple pointlike targets. In the case of distributed targets, instead, it will be provided a detailed analysis of the random fluctuations of the reconstructed scene as a function of the extent of the PDs, of the vertical structure of the imaged scene, and of the number of looks that are employed. Results from Monte Carlo simulations will be presented that fully support the theoretical developments within this paper. The most relevant conclusion of this paper is that the impact of PDs is more severe in the case where the imaged scene is characterized by a complex vertical structure or when multiple pointlike targets are present. As a consequence, it follows that the T-SAR analyses require either a higher phase stability or a more accurate phase calibration with respect to InSAR analyses. Finally, an example of phase-stability analysis and phase calibration of a real data set will be shown, based on a P-band data set relative to the forest site of Remningstorp, Sweden. View full abstract»

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  • Height Retrieval of Isolated Buildings From Single High-Resolution SAR Images

    Page(s): 2967 - 2979
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (847 KB) |  | HTML iconHTML  

    Detection of man-made structures in urban areas, in terms of both geometric and electromagnetic features, from a single, possibly high resolution (HR), synthetic aperture radar (SAR) image is a highly interesting open challenge. Within this framework, a possible approach for the extraction of some relevant parameters, describing the shape and materials of a generic building, is proposed here. The approach is based on sound electromagnetic models for the radar returns of each element of the urban scene. A fully analytical representation of electromagnetic returns from the scene constituents to an active microwave sensor is employed. Some possible applications of feature extractions from real SAR images, based on the aforementioned approach, have already been presented in the literature as first examples of potentiality of a model-based approach, but here, the overall theory is analyzed and discussed in depth, to move to general considerations about its soundness and applicability, and the efficiency of further applications may be derived. For the sake of conciseness, although the proposed approach is general and can be applied for the retrieval of different scene parameters (in principle, anyone contributing to the radar return), we focus here on the extraction of the building height, and we assume that the other parameters are either a priori known (e.g., electromagnetic properties of the materials) or have been previously retrieved from the same SAR image (e.g., building length and width). An analysis of the sensitiveness of the height retrieval to both model inaccuracies and errors on the knowledge of the other parameters is performed. Some simulation examples accompany and validate the solution scheme that we propose. View full abstract»

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  • Two Novel Bayesian Multiscale Approaches for Speckle Suppression in SAR Images

    Page(s): 2980 - 2993
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (873 KB) |  | HTML iconHTML  

    Speckle suppression is a prerequisite for many synthetic aperture radar (SAR) image-processing tasks. Previously, we introduced a Bayesian-based speckle-suppression method that employed the 2-D generalized autoregressive conditional heteroscedasticity (2D-GARCH) model for wavelet coefficients of log-transformed SAR images. Based on this method, we propose two new Bayesian speckle-suppression approaches in this paper. In the first approach, we introduce a new heteroscedastic model, i.e., the 2D-GARCH Mixture (2D-GARCH-M) model, as an extension of the 2D-GARCH model. This new model can capture the characteristics of wavelet coefficients. Also, the 2D-GARCH-M model introduces additional flexibility in the model formulation in comparison with the 2D-GARCH model, which results in better characterization of SAR image subbands and improved restoration in noisy environments. Then, we design a Bayesian estimator for estimating the clean-image wavelet coefficients based on 2D-GARCH-M modeling. In the second approach, the logarithm of an image is analyzed by means of the curvelet transform instead of wavelet transform. Then, we study the statistical properties of curvelet coefficients, and we demonstrate that the 2D-GARCH model can capture the characteristics of curvelet coefficients, such as heavy tailed marginal distribution, and the dependences among them. Consequently, under the 2D-GARCH model, we design a Bayesian estimator for estimating the clean-image curvelet coefficients. Finally, we compare these methods with other denoising methods applied on artificially speckled and actual SAR images, and we verify the performance improvement in utilizing the new strategies. View full abstract»

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  • Multichannel Azimuth Processing in ScanSAR and TOPS Mode Operation

    Page(s): 2994 - 3008
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1529 KB) |  | HTML iconHTML  

    Due to a system-inherent limitation, conventional synthetic aperture radar (SAR) is incapable of imaging a wide swath with high geometric resolution. This restriction can be overcome by systems with multiple receive channels in combination with an additional digital signal processing network. So far, the application of such digital beamforming algorithms for high-resolution wide-swath SAR imaging has been restricted to multichannel systems in stripmap operation. However, in stripmap mode, the overall azimuth antenna length restricts the achievable swath width, thus preventing very wide swaths as requested by future SAR missions. Consequently, new concepts for ultrawide-swath imaging are needed. A promising candidate is a SAR system with multiple azimuth channels being operated in burst mode. This paper analyzes innovative ScanSAR and Terrain Observation by Progressive Scans (TOPS) system concepts with regard to multichannel azimuth processing. For this, the theoretical analyses, performance figures, and SAR signal processing, which had previously been derived for multichannel stripmap mode, are extended to systems operating in burst modes. The investigations reveal that multichannel ScanSAR systems enable the imaging of ultrawide swaths with high azimuth resolution and compact antenna lengths. These considerations are embedded in a multichannel ScanSAR system design example to demonstrate its capability to image an ultrawide swath of 400 km with a high geometric resolution of 5 m. In a next step, this system is adapted to TOPS mode operation, including an innovative “staircase” multichannel processing approach optimized for TOPS. View full abstract»

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  • Potentials and Limitations of Moon-Borne SAR Imaging

    Page(s): 3009 - 3019
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (501 KB) |  | HTML iconHTML  

    Moon exploitation is among the next space mission priorities. Earth observation (EO), which is traditionally implemented on artificial lower Earth orbit satellites, can be, in principle, extended to the platform constituted by the natural Earth satellite. With this regard, we investigate the features related to the EO by a possible Moon-borne synthetic aperture radar system in terms of imaging characteristics and potential applications, as well as of expected limitations. View full abstract»

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  • Topographic Correction for ALOS PALSAR Interferometry

    Page(s): 3020 - 3027
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2351 KB) |  | HTML iconHTML  

    L-band synthetic aperture radar (SAR) interferometry is very successful for mapping ground deformation in densely vegetated regions. However, due to its larger wavelength, the capacity to detect slow deformation over a short period of time is limited. Stacking and small baseline subset (SBAS) techniques are routinely used to produce time series of deformation and average deformation rates by reducing the contribution of topographic and atmospheric noise. For large sets of images that are presently available from C-band European Remote Sensing Satellites (ERS-1/2) and Environmental Satellite (ENVISAT), the standard stacking and SBAS algorithms are accurate. However, the same algorithms are often inaccurate when used for processing of interferograms from L-band Advanced Land Observing Satellite Phased Array type L-band SAR (ALOS PALSAR). This happens because only a limited number of interferograms is acquired and also because of large spatial baselines often correlated with the time of acquisition. In this paper two techniques are suggested that can be used for removing the residual topographic component from stacking and SBAS results, thereby increasing their accuracy. View full abstract»

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Aims & Scope

 

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

 

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Meet Our Editors

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