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FUSION 2018, 21th International Conference on Information Fusion 10/07/2018 13/07/2018 Cambridge Royaume-Uni Proceedings IEEE
nom du congrès :
FUSION 2018, 21th International Conference on Information Fusion
début du congrès :
10/07/2018
fin du congrès :
13/07/2018
ville du congrès :
Cambridge
pays du congrès :
Royaume-Uni
site des actes du congrès :
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Documents disponibles (1)
Ajouter le résultat dans votre panier Affiner la recherche Interroger des sources externesGeometric multi-wavelet total variation for SAR image time series analysis / Abdourrahmane M. Atto (2018)
Titre : Geometric multi-wavelet total variation for SAR image time series analysis Type de document : Article/Communication Auteurs : Abdourrahmane M. Atto, Auteur ; Anoumou Kemavo, Auteur ; Jean-Paul Rudant Editeur : Chambéry : Université de Savoie Année de publication : 2018 Conférence : FUSION 2018, 21th International Conference on Information Fusion 10/07/2018 13/07/2018 Cambridge Royaume-Uni Proceedings IEEE Projets : PHOENIX / Atto, Abdourrahmane M. Importance : pp Note générale : bibliographie
Projet PHOENIX ANR-15-CE23-00Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] Amazonie
[Termes IGN] forêt tropicale
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] Oyapoc (fleuve)
[Termes IGN] série temporelleRésumé : (auteur) A time series issued from modern synthetic aperture radar satellite imaging sensors is a huge dataset composed by many hundreds of million pixels when observing large-scale earth structures such as big forests or glaciers. A concise monitoring of these large scale structures for anomaly spotting thus requires loading and analyzing huge spatio/polarimetric multi-temporal image series. The contributions of the present paper for the sake of parsimonious analysis of such huge datasets are associated with a framework having two main processing stages. The first stage is the derivation of an index called geometric multi-wavelet total variation for fast and robust anomaly spotting. This index is useful for identifying significant abnormal patterns appearing as geo-spatial non-stationarities in multi-wavelet total variation map. The second stage consists in the proposal of a concise asymmetric multi-date change information matrix on regions associated with significant multi-wavelet total variations. This stage is necessary for a fine characterization of change impacts on existing geo-spatial structures. Experimental tests based on Sentinel-1 data show relevant results on a wide Amazonian forest surrounding the Franco-Brazilian Oyapock Bridge. Numéro de notice : C2018-125 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.23919/ICIF.2018.8455223 Date de publication en ligne : 06/09/2018 En ligne : https://doi.org/10.23919/ICIF.2018.8455223 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100016
