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A combined object- and pixel-based image analysis framework for urban land cover classification of VHR imagery / Bahram Salehi in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 11 (November 2013)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 79 n° 11 (November 2013) . - pp 999 - 1014 Titre : A combined object- and pixel-based image analysis framework for urban land cover classification of VHR imagery Type de document : Article/Communication Auteurs : Bahram Salehi, Auteur ; Yun Zhang, Auteur ; Ming Zhong, Auteur Année de publication : 2013 Article en page(s) : pp 999 - 1014 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte d'occupation du sol [Termes IGN] classification par maximum de vraisemblance [Termes IGN] extraction de traits caractéristiques [Termes IGN] image Ikonos [Termes IGN] image Quickbird [Termes IGN] image Worldview [Termes IGN] Nouveau-Brunswick (Canada) [Termes IGN] occupation du sol [Termes IGN] segmentation d'image [Termes IGN] transformation en ondelettes Résumé : (Auteur) This paper aims at exploiting the advantages of pixel-based and object-based image analysis approaches for urban land cover classification of very high resolution ( VHR ) satellite imagery through a combined object- and pixel-based image analysis framework. The framework starts with segmenting the image resulting in several spectral and spatial features of segments. To overcome the curse of dimensionality, a wavelet- based feature extraction method is proposed to reduce the number of features. The wavelet-based method is automatic, fast, and can preserve local variations in objects' spectral/ spatial signatures. Finally, the extracted features together with the original bands of the image are classified using the conventional pixel-based Maximum Likelihood classification. The proposed method was tested on the WorldView-2, QuickBird, and Ikonos images of the same urban area for comparison purposes. Results show up to 17 percent, 10 percent, and 11 percent improvement in kappa coefficients compared to the case in which only the original bands of the image are used for WV - 2 , QB , and IK , respectively. Furthermore, the objects' spectral features contribute more to increasing classification accuracy than spatial features. Numéro de notice : A2013-596 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.79.11.999 En ligne : https://doi.org/10.14358/PERS.79.11.999 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32732 [article]