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Automatic building detection using the Dempster-Shafer algorithm / Y.H. Lu in Photogrammetric Engineering & Remote Sensing, PERS, vol 72 n° 4 (April 2006)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 72 n° 4 (April 2006) . - pp 395 - 402 Titre : Automatic building detection using the Dempster-Shafer algorithm Type de document : Article/Communication Auteurs : Y.H. Lu, Auteur ; John C. Trinder, Auteur ; K. Kubik, Auteur Année de publication : 2006 Article en page(s) : pp 395 - 402 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse d'image numérique [Termes IGN] bâtiment [Termes IGN] classification de Dempster-Shafer [Termes IGN] classification dirigée [Termes IGN] classification par maximum de vraisemblance [Termes IGN] détection de changement [Termes IGN] détection du bâti [Termes IGN] fusion d'images [Termes IGN] modèle numérique de surface [Termes IGN] photo-interprétation assistée par ordinateur Résumé : (Auteur) An approach and strategy for automatic detection of buildings from aerial images using combined image analysis and interpretation techniques is described in this paper. It is undertaken in several steps. A dense DSM is obtained by stereo image matching and then the results of multi-band classification, the DSM, and Normalized Difference Vegetation Index (ndvi) are used to reveal preliminary building interest areas. From these areas, a shape modeling algorithm has been used to precisely delineate their boundaries. The Dempster-Shafer data fusion technique is then applied to detect buildings from the combination of three data sources by a statistically-based classification. A number of test areas, which include buildings of different sizes, shape, and roof color have been investigated. The tests are encouraging and demonstrate that all processes in this system are important for effective building detection. Numéro de notice : A2006-121 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.72.4.395 En ligne : https://doi.org/10.14358/PERS.72.4.395 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27848 [article]