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Introducing spatial regularization in SAR tomography reconstruction / Clément Rambour in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 57 n° 11 (November 2019) . - pp 8600 - 8617 Titre : Introducing spatial regularization in SAR tomography reconstruction Type de document : Article/Communication Auteurs : Clément Rambour, Auteur ; Loïc Denis, Auteur ; Florence Tupin, Auteur ; Hélène Oriot, Auteur Année de publication : 2019 Article en page(s) : pp 8600 - 8617 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] acquisition comprimée [Termes descripteurs IGN] analyse spectrale [Termes descripteurs IGN] écho radar [Termes descripteurs IGN] fractionnement [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image TerraSAR-X [Termes descripteurs IGN] mécanique de Lagrange [Termes descripteurs IGN] reconstruction 3D du bâti [Termes descripteurs IGN] scène urbaine [Termes descripteurs IGN] TerraSAR-X [Termes descripteurs IGN] tomographie radar Résumé : (auteur) The resolution achieved by current synthetic aperture radar (SAR) sensors provides a detailed visualization of urban areas. Spaceborne sensors such as TerraSAR-X can be used to analyze large areas at a very high resolution. In addition, repeated passes of the satellite give access to temporal and interferometric information on the scene. Because of the complex 3-D structure of urban surfaces, scatterers located at different heights (ground, building facade, and roof) produce radar echoes that often get mixed within the same radar cells. These echoes must be numerically unmixed in order to get a fine understanding of the radar images. This unmixing is at the core of SAR tomography. SAR tomography reconstruction is generally performed in two steps: 1) reconstruction of the so-called tomogram by vertical focusing, at each radar resolution cell, to extract the complex amplitudes (a 1-D processing) and 2) transformation from radar geometry to ground geometry and extraction of significant scatterers. We propose to perform the tomographic inversion directly in ground geometry in order to enforce spatial regularity in 3-D space. This inversion requires solving a large-scale nonconvex optimization problem. We describe an iterative method based on variable splitting and the augmented Lagrangian technique. Spatial regularizations can easily be included in this generic scheme. We illustrate, on simulated data and a TerraSAR-X tomographic data set, the potential of this approach to produce 3-D reconstructions of urban surfaces. Numéro de notice : A2019-596 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2921756 date de publication en ligne : 04/07/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2921756 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94588 [article]

Sparse signal modeling: Application to image compression, Image error concealment and compressed sensing / Ali Akbari (2018)  

Public Titre : Sparse signal modeling: Application to image compression, Image error concealment and compressed sensing Type de document : Thèse/HDR Auteurs : Ali Akbari, Auteur Editeur : Paris : Sorbonne Université Année de publication : 2018 Importance : 158 p. Format : 21 x 30 cm Note générale : bibliographie Thèse de Doctorat de Traitement du signal et de l’image, Sorbonne Université Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes descripteurs IGN] acquisition comprimée [Termes descripteurs IGN] compensation [Termes descripteurs IGN] compression d'image [Termes descripteurs IGN] modélisation [Termes descripteurs IGN] problème inverse [Termes descripteurs IGN] reconstruction d'image [Termes descripteurs IGN] représentation parcimonieuse [Termes descripteurs IGN] théorie du signal Résumé : (auteur) Signal models are a cornerstone of contemporary signal and image processing methodology. In this report, two particular signal modeling methods, called analysis and synthesis sparse representation, are studied which have been proven to be effective for many signals, such as natural images, and successfully used in a wide range of applications. Both models represent signals in terms of linear combinations of an underlying set, called dictionary, of elementary signals known as atoms. The driving force behind both models is sparsity of the representation coefficients, i.e. the rapid decay of the representation coefficients over the dictionary. On the other hands, the dictionary choice determines the success of the entire model. According to these two signal models, there have been two main disciplines of dictionary designing; harmonic analysis approach and machine learning methodology. The former leads to designing the dictionaries with easy and fast implementation, while the latter provides a simple and expressive structure for designing adaptable and efficient dictionaries. The main goal of this thesis is to provide new applications to these signal modeling methods by addressing several problems from various perspectives. It begins with the direct application of the sparse representation, i.e. image compression. The line of research followed in this area is the synthesis-based sparse representation approach in the sense that the dictionary is not fixed and predefined, but learned from training data and adapted to data, yielding a more compact representation. A new Image codec based on adaptive sparse representation over a trained dictionary is proposed, wherein different sparsity levels are assigned to the image patches belonging to the salient regions, being more conspicuous to the human visual system. Experimental results show that the proposed method outperforms the existing image coding standards, such as JPEG and JPEG2000, which use an analytic dictionary, as well as the state-of-the-art codecs based on the trained dictionaries. In the next part of thesis, it focuses on another important application of the sparse signal modeling, i.e. solving inverse problems, especially for error concealment (EC), wherein a corrupted image is reconstructed from the incomplete data, and Compressed Sensing recover, where an image is reconstructed from a limited number of random measurements. Signal modeling is usually used as a prior knowledge about the signal to solve these NP-hard problems. In this thesis, inspired by the analysis and synthesis sparse models, these challenges are transferred into two distinct sparse recovery frameworks and several recovery methods are proposed. Compared with the state-of-the-art EC and CS algorithms, experimental results show that the proposed methods show better reconstruction performance in terms of objective and subjective evaluations. This thesis is finalized by giving some conclusions and introducing some lines for future works. Note de contenu : 1- Introduction 2- Sparsity-based signal models 3- Image compressed sensing recovery 4- Receiver-based error concealment based on synthesis sparse recovery 5- Transmitter-based error concealment based on sparse recovery 6- Sparse representation-based image compression 7- Conclusion and future directions Numéro de notice : 25937 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Spécialité : Traitement du signal et de l’image : Paris : 2018 DOI : sans En ligne : http://www.theses.fr/2018SORUS461 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96286

Multibaseline polarimetric synthetic aperture radar tomography of forested areas using wavelet-based distribution compressive sensing / Lei Liang in Journal of applied remote sensing [en ligne], vol 9 (2015)  

Public [article]  inJournal of applied remote sensing [en ligne] > vol 9 (2015) Titre : Multibaseline polarimetric synthetic aperture radar tomography of forested areas using wavelet-based distribution compressive sensing Type de document : Article/Communication Auteurs : Lei Liang, Auteur ; Xinwu Li, Auteur ; Xizhang Gao, Auteur ; Huadong Guo, Auteur Année de publication : 2015 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] acquisition comprimée [Termes descripteurs IGN] bande P [Termes descripteurs IGN] données polarimétriques [Termes descripteurs IGN] forêt tropicale [Termes descripteurs IGN] Guyane (département français) [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] modélisation 3D [Termes descripteurs IGN] structure d'un peuplement forestier [Termes descripteurs IGN] tomographie radar Résumé : (auteur) The three-dimensional (3-D) structure of forests, especially the vertical structure, is an important parameter of forest ecosystem modeling for monitoring ecological change. Synthetic aperture radar tomography (TomoSAR) provides scene reflectivity estimation of vegetation along elevation coordinates. Due to the advantages of super-resolution imaging and a small number of measurements, distribution compressive sensing (DCS) inversion techniques for polarimetric SAR tomography were successfully developed and applied. This paper addresses the 3-D imaging of forested areas based on the framework of DCS using fully polarimetric (FP) multibaseline SAR interferometric (MB-InSAR) tomography at the P-band. A new DCS-based FP TomoSAR method is proposed: a new wavelet-based distributed compressive sensing FP TomoSAR method (FP-WDCS TomoSAR method). The method takes advantage of the joint sparsity between polarimetric channel signals in the wavelet domain to jointly inverse the reflectivity profiles in each channel. The method not only allows high accuracy and super-resolution imaging with a low number of acquisitions, but can also obtain the polarization information of the vertical structure of forested areas. The effectiveness of the techniques for polarimetric SAR tomography is demonstrated using FP P-band airborne datasets acquired by the ONERA SETHI airborne system over a test site in Paracou, French Guiana. Numéro de notice : A2015-738 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article En ligne : http://remotesensing.spiedigitallibrary.org/article.aspx?articleid=2466931 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78439 [article]

High-resolution fully polarimetric ISAR imaging based on compressive sensing / Wei Qiu in IEEE Transactions on geoscience and remote sensing, vol 52 n° 10 tome 1 (October 2014)

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 10 tome 1 (October 2014) . - pp 6119 - 6131 Titre : High-resolution fully polarimetric ISAR imaging based on compressive sensing Type de document : Article/Communication Auteurs : Wei Qiu, Auteur ; H. Zhao, Auteur ; J. Zhou, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 6119 - 6131 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s) [Termes descripteurs IGN] acquisition comprimée [Termes descripteurs IGN] image ISAR [Termes descripteurs IGN] image radar [Termes descripteurs IGN] polarimétrie radar Résumé : (Auteur) A 2-D range/cross-range radar image of a target is always sparse since only a few strong scattering centers occupy the whole image plane, and thus, it is quite suitable to apply the compressive sensing (CS) theory to obtain inverse synthetic aperture radar (ISAR) images. In this paper, a novel fully polarimetric ISAR imaging method based on CS is proposed. First, a definition of joint sparsity is given by exploiting the scattering characteristics of a target in fully polarimetric channels. Then, fully polarimetric ISAR images are constructed by means of the sparse recovery algorithm under the constraint of the joint sparsity. This proposed imaging method combines the merits of a full-polarization technique and CS theory, and hence, it has two main advantages: it can provide high-resolution ISAR images with limited measurements, which is a promising technique for reducing data storage; it generates fully polarimetric ISAR images with the number and the positions of the scattering centers aligned in polarimetric channels, which allows for further polarimetric scattering characteristic analysis. Finally, both simulation and experimental results are shown to demonstrate the validity of the proposed approach. Numéro de notice : A2014-486 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74075 [article]

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Joint wall mitigation and compressive sensing for indoor image reconstruction / E. Lagunas in IEEE Transactions on geoscience and remote sensing, vol 51 n° 2 (February 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 2 (February 2013) . - pp 891 - 906 Titre : Joint wall mitigation and compressive sensing for indoor image reconstruction Type de document : Article/Communication Auteurs : E. Lagunas, Auteur ; M. Armin, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 891 - 906 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] acquisition comprimée [Termes descripteurs IGN] carte d'intérieur [Termes descripteurs IGN] cible mobile [Termes descripteurs IGN] détection à travers-le-mur [Termes descripteurs IGN] fouillis d'échos [Termes descripteurs IGN] image radar [Termes descripteurs IGN] positionnement en intérieur [Termes descripteurs IGN] reconstruction d'image Résumé : (Auteur) Compressive sensing (CS) for urban operations and through-the-wall radar imaging has been shown to be successful in fast data acquisition and moving target localizations. The research in this area thus far has assumed effective removal of wall electromagnetic backscatterings prior to CS application. Wall clutter mitigation can be achieved using full data volume which is, however, in contradiction with the underlying premise of CS. In this paper, we enable joint wall clutter mitigation and CS application using a reduced set of spatial-frequency observations in stepped frequency radar platforms. Specifically, we demonstrate that wall mitigation techniques, such as spatial filtering and subspace projection, can proceed using fewer measurements. We consider both cases of having the same reduced set of frequencies at each of the available antenna locations and also when different frequency measurements are employed at different antenna locations. The latter casts a more challenging problem, as it is not amenable to wall removal using direct implementation of filtering or projection techniques. In this case, we apply CS at each antenna individually to recover the corresponding range profile and estimate the scene response at all frequencies. In applying CS, we use prior knowledge of the wall standoff distance to speed up the convergence of the orthogonal matching pursuit for sparse data reconstruction. Real data are used for validation of the proposed approach. Numéro de notice : A2013-084 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2203824 En ligne : https://doi.org/10.1109/TGRS.2012.2203824 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32222 [article]

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