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A methodology for near real-time change detection between Unmanned Aerial Vehicle and wide area satellite images / Anastasios L. Fytsilis in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 119 (September 2016) . - pp 165- 186 Titre : A methodology for near real-time change detection between Unmanned Aerial Vehicle and wide area satellite images Type de document : Article/Communication Auteurs : Anastasios L. Fytsilis, Auteur ; Anthony Prokos, Auteur ; Konstantinos D. Koutroumbas, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 165- 186 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification hybride [Termes IGN] drone [Termes IGN] gradient [Termes IGN] image aérienne [Termes IGN] image satellite [Termes IGN] méthodologie [Termes IGN] orthorectification automatique [Termes IGN] recalage d'image Résumé : (Auteur) In this paper a novel integrated hybrid methodology for unsupervised change detection between Unmanned Aerial Vehicle (UAV) and satellite images, which can be utilized in various fields like security applications (e.g. border surveillance) and damage assessment, is proposed. This is a challenging problem mainly due to the difference in geographic coverage and the spatial resolution of the two images, as well as to the acquisition modes which lead to misregistration errors. The methodology consists of the following steps: (a) pre-processing, where the part of the satellite image that corresponds to the UAV image is determined and the UAV image is ortho-rectified using information provided by a Digital Terrain Model, (b) the detection of potential changes, which is based exclusively on intensity and image gradient information, (c) the generation of the region map, where homogeneous regions are produced by the previous potential changes via a seeded region growing algorithm and placed on the region map, and (d) the evaluation of the above regions, in order to characterize them as true changes or not. The methodology has been applied on demanding real datasets with very encouraging results. Finally, its robustness to the misregistration errors is assessed via extensive experimentation. Numéro de notice : A2016-782 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.06.001 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2016.06.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82479 [article]