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Detecting new buildings from aerial stereo pairs at different dates / Franck Jung (2001)  

Public Titre : Detecting new buildings from aerial stereo pairs at different dates Type de document : Article/Communication Auteurs : Franck Jung , Auteur Editeur : Washington : Society of Photo-Optical Instrumentation Engineers SPIE Année de publication : 2001 Conférence : SPIE 2001, Applications of digital image Processing 24 31/07/2001 03/08/2001 San Diego Californie - Etats-Unis Proceedings SPIE Importance : pp 21 - 29 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse comparative [Termes IGN] arbre de décision [Termes IGN] bati [Termes IGN] classification [Termes IGN] couple stéréoscopique [Termes IGN] détection de changement [Termes IGN] modèle numérique de surface [Termes IGN] zone d'intérêt Résumé : (auteur) The aim of this application is to detect changes in an aerial scene by comparing stereo pairs taken at intervals of several years in order to update a database. The result is a set of image locations that have a high likelihood to contain changes. Each location will be submitted to a human operator who will either validate the given change and update the database or reject it. We are mainly interested in changes occurring for a specific class of objects: buildings. To isolate new construction, we provide an algorithm that works in two steps. First, during a focusing phase, we aim to eliminate a large part of the scene without losing any actual changes. This is achieved with a Digital Elevation Model (DEM) comparison between the two different dates. Then, in the second phase, we classify regions of interest (ROI). Each ROI is described by four images: a stereo pair of the focusing area at the first date and a stereo pair of the focusing area at the second date. To decide whether or not the ROI contains a change, we classify each of the four images as building or non-building. The building vs non-building classifier is a combination of several decision trees induced by learning. Each node of a decision tree is identified with a graph of features which is more likely to describe buildings than background. Finally, the classification results at the two different dates are compared. Numéro de notice : C2001-053 Affiliation des auteurs : MATIS (1993-2011) Thématique : IMAGERIE Nature : Communication DOI : 10.1117/12.449758 En ligne : https://doi.org/10.1117/12.449758 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86024