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A matching algorithm for detecting land use changes using case-based reasoning / X. Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 75 n° 11 (November 2009)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 75 n° 11 (November 2009) . - pp 1319 - 1332 Titre : A matching algorithm for detecting land use changes using case-based reasoning Type de document : Article/Communication Auteurs : X. Li, Auteur ; A.G. Yeh, Auteur ; J. Qian, Auteur ; Z. Qi, Auteur Année de publication : 2009 Article en page(s) : pp 1319 - 1332 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] algorithme génétique [Termes IGN] analyse diachronique [Termes IGN] analyse spatiale [Termes IGN] classification barycentrique [Termes IGN] classification orientée objet [Termes IGN] détection automatique [Termes IGN] détection de changement [Termes IGN] image multitemporelle [Termes IGN] image radar [Termes IGN] image Radarsat [Termes IGN] segmentation d'image [Termes IGN] utilisation du sol Résumé : (Auteur) The paper deals with change detection using time series SAR images. SAR provides a unique opportunity for detecting land-use changes within short intervals (e.g., monthly) in tropical and sub-tropical regions with cloud cover. Traditional change detection methods mainly rely on per-pixel spectral information but ignore per-object structural information. In this study, a new method is presented that integrates object-oriented analysis with case-based reasoning (CBR) for change detection. Object-oriented analysis is carried out to retrieve a variety of features, such as tone, shape, texture, area, and context. An incremental segmentation technique is proposed for deriving change objects from multi-temporal Radarsat images. Feature selection based on genetic algorithms is carried out to determine the optimal set of features for change detection. A CBR matching algorithm is developed to identify the temporal positions and the kind of changes. It is based on the weighted k-Nearest Neighbor classification using an accumulative similarity measure. The comparison of the four combinations of change detection methods, object-based or pixel-based plus case-based or rule-based, is carried out to validate the performance of this proposed method. The analysis shows that this integrated approach has provided an efficient way of detecting land-use changes at monthly intervals by using multi-temporal SAR images. Copyright ASPRS Numéro de notice : A2009-443 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.75.11.1319 En ligne : https://doi.org/10.14358/PERS.75.11.1319 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30074 [article]