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Object-based building change detection from a single multispectral image and pre-existing geospatial information / Georgia Doxani in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 6 (June 2015)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 81 n° 6 (June 2015) . - pp 481 - 489 Titre : Object-based building change detection from a single multispectral image and pre-existing geospatial information Type de document : Article/Communication Auteurs : Georgia Doxani, Auteur ; Konstantinos Karantzalos, Auteur ; Maria Tsakiri-Strati, Auteur Année de publication : 2015 Article en page(s) : pp 481 - 489 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] analyse de l'existant [Termes IGN] analyse diachronique [Termes IGN] base de données localisées [Termes IGN] bâtiment [Termes IGN] classification à base de connaissances [Termes IGN] détection de changement [Termes IGN] Grèce [Termes IGN] image isolée [Termes IGN] image multibande [Termes IGN] milieu urbain Résumé : (auteur) Multispectral images of very high spatial resolution and vector data from geospatial databases, such as cadastral maps, are among the cost-effective and broadly available geodata in urban environments. Therefore, we aim to address building change detection based on pre-existing building footprint information and a single very high resolution multispectral image. An object-based classification methodology was developed that employs advanced scalespace filtering, unsupervised clustering, and knowledge-based classification. The developed framework effectively integrates prior vector data and multispectral observations, through incorporating the prior knowledge into the training process and defining the proper object-based classification rules. The methodology successfully identified important building changes, which were validated by employing the vector information of a building geodatabase and a QuickBird image acquired in 2003 and 2007, respectively, over urban regions in the city of Thessaloniki, Greece. The performed quantitative and qualitative evaluation indicated that the proposed analysis framework can detect the new buildings with high accuracy rates and, to a lesser degree, their exact shape and size. Numéro de notice : A2015-978 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.81.6.481 En ligne : https://doi.org/10.14358/PERS.81.6.481 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80061 [article]