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On the optimization and selection of wavelet texture for feature extraction from high-resolution satellite imagery with application towards urban-tree delineation / Y.O. Ouma in International Journal of Remote Sensing IJRS, vol 27 n°1-2 (January 2006)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 27 n°1-2 (January 2006) . - pp 73 - 104 Titre : On the optimization and selection of wavelet texture for feature extraction from high-resolution satellite imagery with application towards urban-tree delineation Type de document : Article/Communication Auteurs : Y.O. Ouma, Auteur ; T.G. Ngigi, Auteur ; et al., Auteur Année de publication : 2006 Article en page(s) : pp 73 - 104 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse texturale [Termes IGN] arbre urbain [Termes IGN] données multiéchelles [Termes IGN] extraction automatique [Termes IGN] extraction de traits caractéristiques [Termes IGN] image à très haute résolution [Termes IGN] image multibande [Termes IGN] image Quickbird [Termes IGN] optimisation (mathématiques) [Termes IGN] texture d'image [Termes IGN] transformation en ondelettes Résumé : (Auteur) Integration of spectral and multi-scale texture is proposed in order to improve the detection and classification of urban-trees from Quickbird imagery. Arguing that spatial -structure semantic information exits at a hierarchy of scales and that texture is a consequence of objects in the hierarchy, multi-scale wavelets decomposition is proposed for the extraction of vertical, horizontal and diagonal texture components. Pre-selection of texture sub-bands is achieved via mean, entropy, variance and second angular moment. The resulting sub-bands are analysed for separability between trees and similarly reflecting features, such as rice-paddy, grass/lawns, open ground and playground, based on KuIlbackLeibler (KL) divergence and Battacharyya distance. The results are ranked and classified with k-means. In comparison with the field data, KL gave the best results with omission and commission error of 4.4%. The proposed methodology has the ability to capture the increased natural variability in reflectance and improved the accuracy by 23.6%, in comparison with spectral-only. Numéro de notice : A2006-059 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160500295885 En ligne : https://doi.org/10.1080/01431160500295885 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27786 [article]

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