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A novel class-specific object-based method for urban change detection using high-resolution remote sensing imagery / Ting Bai in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 4 (April 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 4 (April 2021) . - pp 249-262 Titre : A novel class-specific object-based method for urban change detection using high-resolution remote sensing imagery Type de document : Article/Communication Auteurs : Ting Bai, Auteur ; Kaimin Sun, Auteur ; Wenzhuo Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 249-262 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] changement d'occupation du sol [Termes IGN] classe d'objets [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] détection de changement [Termes IGN] détection du bâti [Termes IGN] image à haute résolution [Termes IGN] milieu urbain [Termes IGN] segmentation multi-échelle Résumé : (Auteur) A single-scale object-based change-detection classifier can distinguish only global changes in land cover, not the more granular and local changes in urban areas. To overcome this issue, a novel class-specific object-based change-detection method is proposed. This method includes three steps: class-specific scale selection, class-specific classifier selection, and land cover change detection. The first step combines multi-resolution segmentation and a random forest to select the optimal scale for each change type in land cover. The second step links multi-scale hierarchical sampling with a classifier such as random forest, support vector machine, gradient-boosting decision tree, or Adaboost; the algorithm automatically selects the optimal classifier for each change type in land cover. The final step employs the optimal classifier to detect binary changes and from-to changes for each change type in land cover. To validate the proposed method, we applied it to two high-resolution data sets in urban areas and compared the change-detection results of our proposed method with that of principal component analysis k-means, object-based change vector analysis, and support vector machine. The experimental results show that our proposed method is more accurate than the other methods. The proposed method can address the high levels of complexity found in urban areas, although it requires historical land cover maps as auxiliary data. Numéro de notice : A2021-332 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.4.249 Date de publication en ligne : 01/04/2021 En ligne : https://doi.org/10.14358/PERS.87.4.249 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97528 [article]

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