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Auteur Wei Tang |
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Atmospheric correction in time-series SAR interferometry for land surface deformation mapping : A case study of Taiyuan, China / Wei Tang in Advances in space research, vol 58 n° 3 (August 2016)
[article]
Titre : Atmospheric correction in time-series SAR interferometry for land surface deformation mapping : A case study of Taiyuan, China Type de document : Article/Communication Auteurs : Wei Tang, Auteur ; Mingsheng Liao, Auteur ; Peng Yuan, Auteur Année de publication : 2016 Article en page(s) : pp 310 - 325 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] Chine
[Termes IGN] correction atmosphérique
[Termes IGN] données météorologiques
[Termes IGN] image Envisat-ASAR
[Termes IGN] interferométrie différentielle
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] retard ionosphèrique
[Termes IGN] retard troposphérique
[Termes IGN] série temporelleRésumé : (auteur) The dominant error source of Synthetic Aperture Radar Interferometry (InSAR) is atmospheric phase screen (APS), resulting in phase delay of the radar signal propagating through the atmosphere. The APS in the atmosphere can be decomposed into stratified and turbulent components. In this paper, we introduced a method to compensate for stratified component in a radar interferogram using ERA-Interim reanalysis products obtained from European Centre for Medium-Range Weather Forecasts (ECMWF). Our comparative results with radiosonde data demonstrated that atmospheric condition from ERA-Interim could produce reasonable patterns of vertical profiles of atmospheric states. The stratified atmosphere shows seasonal changes which are correlated with time. It cannot be properly estimated by temporal high-pass filtering which assumes that atmospheric effects are random in time in conventional persistent scatterer InSAR (PSI). Thus, the estimated deformation velocity fields are biased. Therefore, we propose the atmosphere-corrected PSI method that the stratified delay are corrected on each interferogram by using ERA-Interim. The atmospheric residuals after correction of stratified delay were interpreted as random variations in space and time which are mitigated by using spatial–temporal filtering. We applied the proposed method to ENVISAT ASAR images covering Taiyuan basin, China, to study the ground deformation associated with groundwater withdrawal. Experimental results show that the proposed method significantly mitigate the topography-correlated APS and the estimated ground displacements agree more closely with GPS measurements than the conventional PSI. Numéro de notice : A2016-590 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.asr.2016.05.003 En ligne : http://dx.doi.org/10.1016/j.asr.2016.05.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81745
in Advances in space research > vol 58 n° 3 (August 2016) . - pp 310 - 325[article]Sparse unmixing of hyperspectral data using spectral a priori information / Wei Tang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)
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Titre : Sparse unmixing of hyperspectral data using spectral a priori information Type de document : Article/Communication Auteurs : Wei Tang, Auteur ; Zhenwei Shi, Auteur ; Ying Wu, Auteur ; Changshui Zhang, Auteur Année de publication : 2015 Article en page(s) : pp 770 - 783 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] classification spectrale
[Termes IGN] image hyperspectraleRésumé : (Auteur) Given a spectral library, sparse unmixing aims at finding the optimal subset of endmembers from it to model each pixel in the hyperspectral scene. However, sparse unmixing still remains a challenging task due to the usually high mutual coherence of the spectral library. In this paper, we exploit the spectral a priori information in the hyperspectral image to alleviate this difficulty. It assumes that some materials in the spectral library are known to exist in the scene. Such information can be obtained via field investigation or hyperspectral data analysis. Then, we propose a novel model to incorporate the spectral a priori information into sparse unmixing. Based on the alternating direction method of multipliers, we present a new algorithm, which is termed sparse unmixing using spectral a priori information (SUnSPI), to solve the model. Experimental results on both synthetic and real data demonstrate that the spectral a priori information is beneficial to sparse unmixing and that SUnSPI can exploit this information effectively to improve the abundance estimation. Numéro de notice : A2015-104 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2328336 En ligne : https://doi.org/10.1109/TGRS.2014.2328336 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75622
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 2 (February 2015) . - pp 770 - 783[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015021 RAB Revue Centre de documentation En réserve L003 Disponible Regularized simultaneous forward–backward greedy algorithm for sparse unmixing of hyperspectral data / Wei Tang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 9 Tome 1 (September 2014)
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Titre : Regularized simultaneous forward–backward greedy algorithm for sparse unmixing of hyperspectral data Type de document : Article/Communication Auteurs : Wei Tang, Auteur ; Zhenwei Shi, Auteur ; Y. Wu, Auteur Année de publication : 2014 Article en page(s) : pp 5271 - 5288 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] image hyperspectrale
[Termes IGN] optimisation (mathématiques)Résumé : (Auteur) Sparse unmixing assumes that each observed signature of a hyperspectral image is a linear combination of only a few spectra (endmembers) in an available spectral library. It then estimates the fractional abundances of these endmembers in the scene. The sparse unmixing problem still remains a great difficulty due to the usually high correlation of the spectral library. Under such circumstances, this paper presents a novel algorithm termed as the regularized simultaneous forward-backward greedy algorithm (RSFoBa) for sparse unmixing of hyperspectral data. The RSFoBa has low computational complexity of getting an approximate solution for the l0 problem directly and can exploit the joint sparsity among all the pixels in the hyperspectral data. In addition, the combination of the forward greedy step and the backward greedy step makes the RSFoBa more stable and less likely to be trapped into the local optimum than the conventional greedy algorithms. Furthermore, when updating the solution in each iteration, a regularizer that enforces the spatial-contextual coherence within the hyperspectral image is considered to make the algorithm more effective. We also show that the sublibrary obtained by the RSFoBa can serve as input for any other sparse unmixing algorithms to make them more accurate and time efficient. Experimental results on both synthetic and real data demonstrate the effectiveness of the proposed algorithm. Numéro de notice : A2014-442 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2287795 En ligne : https://doi.org/10.1109/TGRS.2013.2287795 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73979
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 9 Tome 1 (September 2014) . - pp 5271 - 5288[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2014091A RAB Revue Centre de documentation En réserve L003 Disponible Subspace matching pursuit for sparse unmixing of hyperspectral data / Zhenwei Shi in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 1 (June 2014)
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Titre : Subspace matching pursuit for sparse unmixing of hyperspectral data Type de document : Article/Communication Auteurs : Zhenwei Shi, Auteur ; Wei Tang, Auteur ; Zhana Duren, Auteur ; Zhiguo Jiang, Auteur Année de publication : 2014 Article en page(s) : pp 3256 - 3274 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] appariement d'images
[Termes IGN] image hyperspectrale
[Termes IGN] vecteur aléatoire multidimensionnelRésumé : (Auteur) Sparse unmixing assumes that each mixed pixel in the hyperspectral image can be expressed as a linear combination of only a few spectra (endmembers) in a spectral library, known a priori. It then aims at estimating the fractional abundances of these endmembers in the scene. Unfortunately, because of the usually high correlation of the spectral library, the sparse unmixing problem still remains a great challenge. Moreover, most related work focuses on the l1 convex relaxation methods, and little attention has been paid to the use of simultaneous sparse representation via greedy algorithms (GAs) (SGA) for sparse unmixing. SGA has advantages such as that it can get an approximate solution for the l0 problem directly without smoothing the penalty term in a low computational complexity as well as exploit the spatial information of the hyperspectral data. Thus, it is necessary to explore the potential of using such algorithms for sparse unmixing. Inspired by the existing SGA methods, this paper presents a novel GA termed subspace matching pursuit (SMP) for sparse unmixing of hyperspectral data. SMP makes use of the low-degree mixed pixels in the hyperspectral image to iteratively find a subspace to reconstruct the hyperspectral data. It is proved that, under certain conditions, SMP can recover the optimal endmembers from the spectral library. Moreover, SMP can serve as a dictionary pruning algorithm. Thus, it can boost other sparse unmixing algorithms, making them more accurate and time efficient. Experimental results on both synthetic and real data demonstrate the efficacy of the proposed algorithm. Numéro de notice : A2014-308 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2272076 En ligne : https://doi.org/10.1109/TGRS.2013.2272076 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33211
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 6 Tome 1 (June 2014) . - pp 3256 - 3274[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2014061A RAB Revue Centre de documentation En réserve L003 Disponible