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Wavelet for urban spatial feature discrimination: comparisons with fractal, spatial autocorrelation, and spatial co-occurrence approaches / Nina S.N. Lam in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 7 (July 2004)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 70 n° 7 (July 2004) . - pp 803 - 812 Titre : Wavelet for urban spatial feature discrimination: comparisons with fractal, spatial autocorrelation, and spatial co-occurrence approaches Type de document : Article/Communication Auteurs : Nina S.N. Lam, Auteur ; S.W. Myint, Auteur ; J.M. Tyler, Auteur Année de publication : 2004 Article en page(s) : pp 803 - 812 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse fractale [Termes IGN] autocorrélation spatiale [Termes IGN] classification dirigée [Termes IGN] image multibande [Termes IGN] matrice [Termes IGN] milieu urbain [Termes IGN] précision de la classification [Termes IGN] transformation en ondelettes Résumé : (Auteur) Traditional image processing techniques have proven inadequate for urban mapping using high spatial resolution remote-sensing images. This study examined and evaluated wavelet transforms for urban texture analysis and image classification using high spatial resolution ATLAS imagery. For the purpose of comparison and to evaluate the effectiveness of the wavelet approaches, two different fractal approaches (isarithm and triangular prism), spatial autocorrelation (Moran's I and Geary's C), and spatial co-occurrence matrix of the selected urban classes were examined using 65 X 65, 33 X 33, and 17 X 17 samples with a pixel size of 2.5 m. Results from this study suggest that a multi-band and multi-level wavelet approach can be used to drastically increase the classification accuracy. The fractal techniques did not provide satisfactory classification accuracy. Spatial autocorrelation and spatial co-occurrence techniques were found to be relatively effective when compared to the fractal approaches. It can be concluded that the wavelet transform approach is the most accurate of all four approaches. Numéro de notice : A2004-273 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.70.7.803 En ligne : https://doi.org/10.14358/PERS.70.7.803 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26800 [article]