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Auteur Richard Bamler |
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Three-dimensional deformation monitoring of urban infrastructure by tomographic SAR using multitrack TerraSAR-X data stacks / Sina Montazeri in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)
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Titre : Three-dimensional deformation monitoring of urban infrastructure by tomographic SAR using multitrack TerraSAR-X data stacks Type de document : Article/Communication Auteurs : Sina Montazeri, Auteur ; Xiao Xiang Zhu, Auteur ; Michael Eineder, Auteur ; Richard Bamler, Auteur Année de publication : 2016 Article en page(s) : pp 6868 - 6878 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] analyse comparative
[Termes IGN] Berlin
[Termes IGN] déformation d'édifice
[Termes IGN] image radar moirée
[Termes IGN] image TerraSAR-X
[Termes IGN] semis de points
[Termes IGN] surveillance d'ouvrage
[Termes IGN] tomographie radarRésumé : (Auteur) Differential synthetic aperture radar tomography (D-TomoSAR), similar to its conventional counterparts such as differential interferometric SAR and persistent scatterer interferometry, is only capable of capturing 1-D deformation along the satellite's line of sight. In this paper, we propose a method based on L1-norm minimization within local spatial cubes to reconstruct 3-D displacement vectors from TomoSAR point clouds available from at least three different viewing geometries. The methodology is applied on two pairs of cross-heading-combination of ascending and descending-TerraSAR-X (TS-X) spotlight image stacks over the city of Berlin. The linear deformation rate and the amplitude of seasonal deformation are decomposed, and the results from two test sites with remarkable deformation pattern are discussed in detail. The results, to our knowledge, demonstrate the first attempt for motion decomposition using TomoSAR data from multiple viewing geometries. Numéro de notice : A2016-919 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2585741 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2585741 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83322
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 6868 - 6878[article]Exploiting joint sparsity for pansharpening : the J-SparseFI algorithm / Xiao Xiang Zhu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)
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Titre : Exploiting joint sparsity for pansharpening : the J-SparseFI algorithm Type de document : Article/Communication Auteurs : Xiao Xiang Zhu, Auteur ; Claas Grohnfeldt, Auteur ; Richard Bamler, Auteur Année de publication : 2016 Article en page(s) : pp 2664 - 2681 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme de fusion
[Termes IGN] données clairsemées
[Termes IGN] fusion d'images
[Termes IGN] image multibande
[Termes IGN] image panchromatique
[Termes IGN] image Worldview
[Termes IGN] reconstruction d'image
[Termes IGN] régularisation de Tychonoff
[Termes IGN] réponse spectraleRésumé : (Auteur) Recently, sparse signal representation of image patches has been explored to solve the pansharpening problem. Although these proposed sparse-reconstruction-based methods lead to promising results, three issues remained unsolved: 1) high computational cost; 2) no consideration given to the possibility of mutually correlated information in different multispectral channels; and 3) requirement that the spectral responses of the panchromatic (Pan) image and the multispectral image cover the same wavelength range, which is not necessarily valid for most sensors. In this paper, we propose a sophisticated sparse image fusion algorithm, which is named “jointly sparse fusion of images” (J-SparseFI). It is based on the earlier proposed sparse fusion of images (SparseFI) algorithm and overcomes the aforementioned three drawbacks of the existing sparse image fusion algorithms. The computational problem is handled by reducing the problem size and by proposing a fully parallelizable scheme. Moreover, J-SparseFI exploits the possible signal structure correlations between multispectral channels by introducing the joint sparsity model (JSM) and sharpening the highly correlated adjacent multispectral channels together. This is done by exploiting the distributed compressive sensing theory that restricts the solution of an underdetermined system by considering an ensemble of signals being jointly sparse. J-SparseFI also offers a practical solution to overcome spectral range mismatch between the Pan and multispectral images. By means of sensor spectral response and channel mutual correlation analysis, the multispectral channels are assigned to primary groups of joint channels, secondary groups of joint channels, and individual channels. Primary groups of joint channels, individual channels, and secondary groups of joint channels are then reconstructed sequentially, by the JSM or by modified SparseFI, using a dictionary trained from the Pan image or previously reconstructed high-resolution multispectral channels. A recipe of how to choose appropriate algorithm parameters, including the most crucial regularization parameter, is provided. The algorithm is evaluated and validated using WorldView-2-like images that are simulated using very high resolution airborne HySpex hyperspectral imagery and further practically demonstrated using real WorldView-2 images. The algorithm's performance is compared with other state-of-the-art methods. Visual and quantitative analyses demonstrate the high quality of the proposed method. In particular, the analysis of the difference images suggests that J-SparseFI is superior in image resolution recovery. Numéro de notice : A2016-844 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2504261 En ligne : http://dx.doi.org/10.1109/TGRS.2015.2504261 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82890
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 5 (May 2016) . - pp 2664 - 2681[article]A sparse image fusion algorithm with application to pan-sharpening / Xiao Xiang Zhu in IEEE Transactions on geoscience and remote sensing, vol 51 n° 5 Tome 1 (May 2013)
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Titre : A sparse image fusion algorithm with application to pan-sharpening Type de document : Article/Communication Auteurs : Xiao Xiang Zhu, Auteur ; Richard Bamler, Auteur Année de publication : 2013 Article en page(s) : pp 2827 - 2836 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] compression d'image
[Termes IGN] fusion d'images
[Termes IGN] image à haute résolution
[Termes IGN] image Geoeye
[Termes IGN] image Ikonos
[Termes IGN] image panchromatique
[Termes IGN] image Quickbird
[Termes IGN] méthode robuste
[Termes IGN] pansharpening (fusion d'images)Résumé : (Auteur) Data provided by most optical Earth observation satellites such as IKONOS, QuickBird, and GeoEye are composed of a panchromatic channel of high spatial resolution (HR) and several multispectral channels at a lower spatial resolution (LR). The fusion of an HR panchromatic and the corresponding LR spectral channels is called “pan-sharpening.” It aims at obtaining an HR multispectral image. In this paper, we propose a new pan-sharpening method named Sparse Fusion of Images (SparseFI, pronounced as “sparsify”). SparseFI is based on the compressive sensing theory and explores the sparse representation of HR/LR multispectral image patches in the dictionary pairs cotrained from the panchromatic image and its downsampled LR version. Compared with conventional methods, it “learns” from, i.e., adapts itself to, the data and has generally better performance than existing methods. Due to the fact that the SparseFI method does not assume any spectral composition model of the panchromatic image and due to the super-resolution capability and robustness of sparse signal reconstruction algorithms, it gives higher spatial resolution and, in most cases, less spectral distortion compared with the conventional methods. Numéro de notice : A2013-259 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2213604 En ligne : https://doi.org/10.1109/TGRS.2012.2213604 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32397
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 5 Tome 1 (May 2013) . - pp 2827 - 2836[article]Réservation
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