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Revealing the anatomy of cities through spectral mixture analysis of multispectral satellite imagery: a case study of the greater Cairo region, Egypt / T. Rashed in Geocarto international, vol 16 n° 4 (December 2001 - February 2002)  

Public [article]  inGeocarto international > vol 16 n° 4 (December 2001 - February 2002) . - pp 5 - 15 Titre : Revealing the anatomy of cities through spectral mixture analysis of multispectral satellite imagery: a case study of the greater Cairo region, Egypt Type de document : Article/Communication Auteurs : T. Rashed, Auteur ; J.R. Weeks, Auteur ; M.S. Gadalla, Auteur ; A. Hill, Auteur Année de publication : 2001 Article en page(s) : pp 5 - 15 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] analyse spectrale [Termes IGN] carte d'occupation du sol [Termes IGN] classification dirigée [Termes IGN] Egypte [Termes IGN] image multibande [Termes IGN] milieu urbain [Termes IGN] occupation du sol Résumé : (Auteur) This paper examines the feasibility of spectral mixture analysis (SMA) in deriving comparable physical measures of urban land cover that describe the morphological characteristics of cities. SMA offers a way of analyzing satellite imagery of urban areas that may be superior to more standard methods of classification. Mixing models are based on the assumption that the remotely measured spectrum of a given pixel can be modeled as a combination of pure spectra, called endmembers. SMA, using four image endmembers (vegetation, impervious surface, soil, and shade), was applied to an IRS-1C multispectral image in order to extract measures that describe the anatomy of the Greater Cairo region, Egypt, in terms of endmember fractions. The resulting fractions were then used to classify the urban scene into eight classes of natural and human-built features through a decision tree (DT) classifier. The accuracy of the DT classification was compared to the accuracies of two per-pixel supervised classifications of the IRS-1C image employing maximum likelihood (ML) and minimum distance-to-means (MDM) classifiers. Overall KAPPA accuracies were 0. 88 for the DT classification based on SMA fractions, and 0.60 and 0.45 for the classifications conducted through ML and MDM respectively. Numéro de notice : A2002-038 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106040108542210 En ligne : https://doi.org/10.1080/10106040108542210 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=21955 [article]

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