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A framework for automatic and unsupervised detection of multiple changes in multitemporal images / Francesca Bovolo in IEEE Transactions on geoscience and remote sensing, vol 50 n° 6 (June 2012)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 50 n° 6 (June 2012) . - pp 2196 - 2212 Titre : A framework for automatic and unsupervised detection of multiple changes in multitemporal images Type de document : Article/Communication Auteurs : Francesca Bovolo, Auteur ; S. Marchesi, Auteur ; Lorenzo Bruzzone, Auteur Année de publication : 2012 Article en page(s) : pp 2196 - 2212 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse diachronique [Termes IGN] bande B [Termes IGN] classification bayesienne [Termes IGN] détection automatique [Termes IGN] détection de changement [Termes IGN] image multibande [Termes IGN] image multitemporelle [Termes IGN] seuillage d'image Résumé : (Auteur) The detection of multiple changes (i.e., different kinds of change) in multitemporal remote sensing images is a complex problem. When multispectral images having B spectral bands are considered, an effective solution to this problem is to exploit all available spectral channels in the framework of supervised or partially supervised approaches. However, in many real applications, it is difficult/impossible to collect ground truth information for either multitemporal or single-date images. On the opposite, unsupervised methods available in the literature are not effective in handling the full information present in multispectral and multitemporal images. They usually consider a simplified subspace of the original feature space having small dimensionality and, thus, characterized by a possible loss of change information. In this paper, we present a framework for the detection of multiple changes in bitemporal and multispectral remote sensing images that allows one to overcome the limits of standard unsupervised methods. The framework is based on the following: 1) a compressed yet efficient 2-D representation of the change information and 2) a two-step automatic decision strategy. The effectiveness of the proposed approach has been tested on two bitemporal and multispectral data sets having different properties. Results obtained on both data sets confirm the effectiveness of the proposed approach. Numéro de notice : A2012-264 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2171493 Date de publication en ligne : 21/11/2011 En ligne : https://doi.org/10.1109/TGRS.2011.2171493 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31710 [article]

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