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A volumetric approach to change in satellite images / T. Pollard in Photogrammetric Engineering & Remote Sensing, PERS, vol 76 n° 7 (July 2010)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 76 n° 7 (July 2010) . - pp 817 - 831 Titre : A volumetric approach to change in satellite images Type de document : Article/Communication Auteurs : T. Pollard, Auteur ; I. Eden, Auteur ; J. Mundy, Auteur ; D. Cooper, Auteur Année de publication : 2010 Article en page(s) : pp 817 - 831 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse de données [Termes IGN] apprentissage automatique [Termes IGN] détection de changement [Termes IGN] données localisées 3D [Termes IGN] image satellite [Termes IGN] modélisation géométrique de prise de vue [Termes IGN] pas d'échantillonnage au sol [Termes IGN] voxel [Termes IGN] zone urbaine dense Résumé : (Auteur) The increasing availability of very high resolution satellite imagery has spurred interest in automatically detecting very fine detailed changes in an area over time, a particularly useful tool for analyzing activity in dense urban areas. However, attempting automated change detection at this resolution is difficult due to the motion parallax of elevated structures. This paper presents a comprehensive solution to change detection in areas of significant 3D relief using a new framework called volumetric appearance modeling (VAM). This approach can manage the complications of unknown and changing world surfaces by maintaining a 3D voxel-based model, where probability distributions for surface occupancy and image appearance are stored in each voxel. These distributions are continuously updated as new images are received using an adaptive learning procedure. This representation is demonstrated to produce accurate change detection results under conditions of variable illumination and viewpoint as well as haze conditions present in satellite imagery. The volumetric representation also supports automatic sensor model correction to align incoming imagery to a common geographic reference. This registration approach is demonstrated to achieve geo-positioning accuracy on the order of the ground sampling distance (GSD) or better. Numéro de notice : A2010-274 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.76.7.817 En ligne : https://doi.org/10.14358/PERS.76.7.817 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30468 [article]