The Experts below are selected from a list of 42 Experts worldwide ranked by ideXlab platform
Roth Achim - One of the best experts on this subject based on the ideXlab platform.
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Curvelet-based Change Detection on SAR Images for Natural Disaster Mapping
E. Schweizerbartsche Verlagsbuchhandlung, 2010Co-Authors: Schmitt Andreas, Wessel Birgit, Roth AchimAbstract:This paper focuses on the use of SAR data in the context of natural disasters. A Curvelet-based change detection algorithm is presented that automatically extracts changes in the radar back-scattering from two TerraSAR-X acquisitions – pre-disaster and post-disaster - of the same area. After a logarithmic scaling of the geocoded amplitude images the Curvelet-transform is applied. The differ-entiation is then done in the Curvelet-Coefficient domain where each Coefficient represents the strength of a linear structure apparent in the original image. In order to reduce noise the resulting Coefficient differences are weighted by a special function that suppresses minor, noise-like structures. The resulting enhanced Coefficients are transformed back to the image domain and brought to the original scaling, so that the values in the difference image describe the increase and the decrease with respect to the amplitude value in the initial image. This approach is applied on three sample data sets: flood, forest fire, and earthquake. For all scenarios including natural landscapes and urban environ-ments as well areas with changes in the radar amplitude are clearly delineated. The interpretation of the changes detected in the radar images needs additional knowledge, e.g. pre-disaster maps. The combination of both could possibly deliver a robust and reliable database for the coordination of res-cue teams after large-scale natural disasters
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Curvelet Approach for SAR Image Denoising, Structure Enhancement, and Change Detection
2009Co-Authors: Schmitt Andreas, Wessel Birgit, Roth AchimAbstract:In this paper we present an alternative method for SAR image denoising, structure enhancement, and change detection based on the Curvelet transform. Curvelets can be denoted as a two dimensional further development of the well-known wavelets. The original image is decomposed into linear ridge-like structures, that appear in different scales (longer or shorter structures), directions (orientation of the structure) and locations. The influence of these single components on the original image is weighted by the corresponding Coefficients. By means of these Coefficients one has direct access to the linear structures present in the image. To suppress noise in a given SAR image weak structures indicated by low Coefficients can be suppressed by setting the corresponding Coefficients to zero. To enhance structures only Coefficients in the scale of interest are preserved and all others are set to zero. Two same-sized images assumed even a change detection can be done in the Curvelet Coefficient domain. The Curvelet Coefficients of both images are differentiated and manipulated in order to enhance strong and to suppress small scale (pixel-wise) changes. After the inverse Curvelet transform the resulting image contains only those structures, that have been chosen via the Coefficient manipulation. Our approach is applied to TerraSAR-X High Resolution Spotlight images of the city of Munich. The Curvelet transform turns out to be a powerful tool for image enhancement in fine-structured areas, whereas it fails in originally homogeneous areas like grassland. In the change detection context this method is very sensitive towards changes in structures instead of single pixel or large area changes. Therefore, for purely urban structures or construction sites this method provides excellent and robust results. While this approach runs without any interaction of an operator, the interpretation of the detected changes requires still much knowledge about the underlying objects
Achim Roth - One of the best experts on this subject based on the ideXlab platform.
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Curvelet APPROACH FOR SAR IMAGE DENOISING, STRUCTURE ENHANCEMENT, AND CHANGE DETECTION
2010Co-Authors: Andreas Schmitt, Birgit Wessel, Achim RothAbstract:In this paper we present an alternative method for SAR image denoising, structure enhancement, and change detection based on the Curvelet transform. Curvelets can be denoted as a two dimensional further development of the well-known wavelets. The original image is decomposed into linear ridge-like structures, that appear in different scales (longer or shorter structures), directions (orientation of the structure) and locations. The influence of these single components on the original image is weighted by the corresponding Coefficients. By means of these Coefficients one has direct access to the linear structures present in the image. To suppress noise in a given SAR image weak structures indicated by low Coefficients can be suppressed by setting the corresponding Coefficients to zero. To enhance structures only Coefficients in the scale of interest are preserved and all others are set to zero. Two same-sized images assumed even a change detection can be done in the Curvelet Coefficient domain. The Curvelet Coefficients of both images are differentiated and manipulated in order to enhance strong and to suppress small scale (pixel-wise) changes. After the inverse Curvelet transform the resulting image contains only those structures, that have been chosen via the Coefficient manipulation. Our approach is applied to TerraSAR-X High Resolution Spotlight images of the city of Munich. The Curvelet transform turns out to be a powerful tool for image enhancement in fine-structured areas, whereas it fails in originally homogeneous areas like grassland. In the change detection context this method is very sensitive towards changes in structures instead of single pixel or large area changes. Therefore, for purely urban structures or construction sites this method provides excellent and robust results. While this approach runs without any interaction o
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Curvelet-based change detection for man-made objects from SAR images
2009 IEEE International Geoscience and Remote Sensing Symposium, 2009Co-Authors: Andreas Schmitt, Birgit Wessel, Achim RothAbstract:In this article we present a technique for fast and robust change detection based on the Curvelet transform. The Curvelet transform is a two dimensional further development of the well-known wavelet transform that reconstructs the original image by ridge-like features, called ridgelets, in different scales, directions and positions. Geocoded SAR amplitude images from TerraSAR-X are compared by differentiating the Coefficients of both images in the Curvelet Coefficient domain. Before the difference image is transformed back to the spatial domain, the influence of the single ridgelets on the resulting image can be manipulated to suppress noise and to intensify structures. Two examples were chosen to show the potential of this approach: a construction site in Germany and an open cast mining area in Chile. Our prototype version is able to compare time series without any interaction of an operator so that the implemented algorithms can easily be embedded into an automatic processing chain.
Schmitt Andreas - One of the best experts on this subject based on the ideXlab platform.
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Curvelet-based Change Detection on SAR Images for Natural Disaster Mapping
E. Schweizerbartsche Verlagsbuchhandlung, 2010Co-Authors: Schmitt Andreas, Wessel Birgit, Roth AchimAbstract:This paper focuses on the use of SAR data in the context of natural disasters. A Curvelet-based change detection algorithm is presented that automatically extracts changes in the radar back-scattering from two TerraSAR-X acquisitions – pre-disaster and post-disaster - of the same area. After a logarithmic scaling of the geocoded amplitude images the Curvelet-transform is applied. The differ-entiation is then done in the Curvelet-Coefficient domain where each Coefficient represents the strength of a linear structure apparent in the original image. In order to reduce noise the resulting Coefficient differences are weighted by a special function that suppresses minor, noise-like structures. The resulting enhanced Coefficients are transformed back to the image domain and brought to the original scaling, so that the values in the difference image describe the increase and the decrease with respect to the amplitude value in the initial image. This approach is applied on three sample data sets: flood, forest fire, and earthquake. For all scenarios including natural landscapes and urban environ-ments as well areas with changes in the radar amplitude are clearly delineated. The interpretation of the changes detected in the radar images needs additional knowledge, e.g. pre-disaster maps. The combination of both could possibly deliver a robust and reliable database for the coordination of res-cue teams after large-scale natural disasters
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Curvelet Approach for SAR Image Denoising, Structure Enhancement, and Change Detection
2009Co-Authors: Schmitt Andreas, Wessel Birgit, Roth AchimAbstract:In this paper we present an alternative method for SAR image denoising, structure enhancement, and change detection based on the Curvelet transform. Curvelets can be denoted as a two dimensional further development of the well-known wavelets. The original image is decomposed into linear ridge-like structures, that appear in different scales (longer or shorter structures), directions (orientation of the structure) and locations. The influence of these single components on the original image is weighted by the corresponding Coefficients. By means of these Coefficients one has direct access to the linear structures present in the image. To suppress noise in a given SAR image weak structures indicated by low Coefficients can be suppressed by setting the corresponding Coefficients to zero. To enhance structures only Coefficients in the scale of interest are preserved and all others are set to zero. Two same-sized images assumed even a change detection can be done in the Curvelet Coefficient domain. The Curvelet Coefficients of both images are differentiated and manipulated in order to enhance strong and to suppress small scale (pixel-wise) changes. After the inverse Curvelet transform the resulting image contains only those structures, that have been chosen via the Coefficient manipulation. Our approach is applied to TerraSAR-X High Resolution Spotlight images of the city of Munich. The Curvelet transform turns out to be a powerful tool for image enhancement in fine-structured areas, whereas it fails in originally homogeneous areas like grassland. In the change detection context this method is very sensitive towards changes in structures instead of single pixel or large area changes. Therefore, for purely urban structures or construction sites this method provides excellent and robust results. While this approach runs without any interaction of an operator, the interpretation of the detected changes requires still much knowledge about the underlying objects
Andreas Schmitt - One of the best experts on this subject based on the ideXlab platform.
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Curvelet APPROACH FOR SAR IMAGE DENOISING, STRUCTURE ENHANCEMENT, AND CHANGE DETECTION
2010Co-Authors: Andreas Schmitt, Birgit Wessel, Achim RothAbstract:In this paper we present an alternative method for SAR image denoising, structure enhancement, and change detection based on the Curvelet transform. Curvelets can be denoted as a two dimensional further development of the well-known wavelets. The original image is decomposed into linear ridge-like structures, that appear in different scales (longer or shorter structures), directions (orientation of the structure) and locations. The influence of these single components on the original image is weighted by the corresponding Coefficients. By means of these Coefficients one has direct access to the linear structures present in the image. To suppress noise in a given SAR image weak structures indicated by low Coefficients can be suppressed by setting the corresponding Coefficients to zero. To enhance structures only Coefficients in the scale of interest are preserved and all others are set to zero. Two same-sized images assumed even a change detection can be done in the Curvelet Coefficient domain. The Curvelet Coefficients of both images are differentiated and manipulated in order to enhance strong and to suppress small scale (pixel-wise) changes. After the inverse Curvelet transform the resulting image contains only those structures, that have been chosen via the Coefficient manipulation. Our approach is applied to TerraSAR-X High Resolution Spotlight images of the city of Munich. The Curvelet transform turns out to be a powerful tool for image enhancement in fine-structured areas, whereas it fails in originally homogeneous areas like grassland. In the change detection context this method is very sensitive towards changes in structures instead of single pixel or large area changes. Therefore, for purely urban structures or construction sites this method provides excellent and robust results. While this approach runs without any interaction o
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Curvelet-based change detection for man-made objects from SAR images
2009 IEEE International Geoscience and Remote Sensing Symposium, 2009Co-Authors: Andreas Schmitt, Birgit Wessel, Achim RothAbstract:In this article we present a technique for fast and robust change detection based on the Curvelet transform. The Curvelet transform is a two dimensional further development of the well-known wavelet transform that reconstructs the original image by ridge-like features, called ridgelets, in different scales, directions and positions. Geocoded SAR amplitude images from TerraSAR-X are compared by differentiating the Coefficients of both images in the Curvelet Coefficient domain. Before the difference image is transformed back to the spatial domain, the influence of the single ridgelets on the resulting image can be manipulated to suppress noise and to intensify structures. Two examples were chosen to show the potential of this approach: a construction site in Germany and an open cast mining area in Chile. Our prototype version is able to compare time series without any interaction of an operator so that the implemented algorithms can easily be embedded into an automatic processing chain.
Neelamani Ramesh - One of the best experts on this subject based on the ideXlab platform.
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Seismic deconvolution revisited with Curvelet frames
European Association of Geoscientists & Engineers, 2005Co-Authors: Hennenfent Gilles, Herrmann, Felix J., Neelamani RameshAbstract:We propose an efficient iterative Curvelet-regularized deconvolution algorithm that exploits continuity along reflectors in seismic images. Curvelets are a new multiscale transform that provides sparse representations for images (such as seismic images) that comprise smooth objects separated by piece-wise smooth discontinuities. Our technique combines conjugate gradient-based convolution operator inversion with noise regularization that is performed using non-linear Curvelet Coefficient shrinkage (thresholding). The shrinkage operation leverages the sparsity of Curvelets representations. Simulations demonstrate that our algorithm provides improved resolution compared to the traditional Wiener-based deconvolution approach.Science, Faculty ofEarth and Ocean Sciences, Department ofUnreviewedGraduateFacult
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Sparseness-constrained seismic deconvolution with Curvelets
Canadian Society of Exploration Geophysicists, 2005Co-Authors: Hennenfent Gilles, Herrmann, Felix J., Neelamani RameshAbstract:Continuity along reflectors in seismic images is used via Curvelet representation to stabilize the convolution operator inversion. The Curvelet transform is a new multiscale transform that provides sparse representations for images that comprise smooth objects separated by piece-wise smooth discontinuities (e.g. seismic images). Our iterative Curvelet-regularized deconvolution algorithm combines conjugate gradient-based inversion with noise regularization performed using non-linear Curvelet Coefficient thresholding. The thresholding operation enhances the sparsity of Curvelet representations. We show on a synthetic example that our algorithm provides improved resolution and continuity along reflectors as well as reduced ringing effect compared to the iterative Wiener-based deconvolution approach.Science, Faculty ofEarth and Ocean Sciences, Department ofUnreviewedGraduateFacult
