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Urban area and building detection using SIFT: Keypoints and Graph Theory / B. Simarcek in IEEE Transactions on geoscience and remote sensing, vol 47 n° 4 (April 2009)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 47 n° 4 (April 2009) . - pp 1156 - 1167 Titre : Urban area and building detection using SIFT: Keypoints and Graph Theory Type de document : Article/Communication Auteurs : B. Simarcek, Auteur ; C. Unsalan, Auteur Année de publication : 2009 Article en page(s) : pp 1156 - 1167 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] appariement de graphes [Termes IGN] détection du bâti [Termes IGN] graphe [Termes IGN] image à résolution métrique [Termes IGN] image Ikonos [Termes IGN] image panchromatique [Termes IGN] milieu urbain [Termes IGN] SIFT (algorithme) [Termes IGN] sommet (mathématique) [Termes IGN] zone urbaine Résumé : (Auteur) Very high resolution satellite images provide valuable information to researchers. Among these, urban-area boundaries and building locations play crucial roles. For a human expert, manually extracting this valuable information is tedious. One possible solution to extract this information is using automated techniques. Unfortunately, the solution is not straightforward if standard image processing and pattern recognition techniques are used. Therefore, to detect the urban area and buildings in satellite images, we propose the use of scale invariant feature transform (SIFT) and graph theoretical tools. SIFT keypoints are powerful in detecting objects under various imaging conditions. However, SIFT is not sufficient for detecting urban areas and buildings alone. Therefore, we formalize the problem in terms of graph theory. In forming the graph, we represent each keypoint as a vertex of the graph. The unary and binary relationships between these vertices (such as spatial distance and intensity values) lead to the edges of the graph. Based on this formalism, we extract the urban area using a novel multiple subgraph matching method. Then, we extract separate buildings in the urban area using a novel graph cut method. We form a diverse and representative test set using panchromatic 1-m-resolution Ikonos imagery. By extensive testings, we report very promising results on automatically detecting urban areas and buildings. Copyright IEEE Numéro de notice : A2009-084 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2008.2008440 En ligne : https://doi.org/10.1109/TGRS.2008.2008440 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29714 [article]

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