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Principal component analysis for hyperspectral image classification / C. Rodarmel in Surveying and land information systems, vol 62 n° 2 (01/06/2002)

Public [article]  inSurveying and land information systems > vol 62 n° 2 (01/06/2002) . - pp 115 - 122 Titre : Principal component analysis for hyperspectral image classification Type de document : Article/Communication Auteurs : C. Rodarmel, Auteur ; J. Shan, Auteur Année de publication : 2002 Article en page(s) : pp 115 - 122 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse en composantes principales [Termes IGN] classification [Termes IGN] image hyperspectrale [Termes IGN] utilisation du sol Résumé : (Auteur) The availability of hyperspectral images expands the capability of using image classification to study detailed characteristics of objects, but at a cost of having to deal with huge data sets. This work studies the use of the principal component analysis as a preprocessing technique for the classification of hyperspectral images. Two hyperspectral data sets, HYDICE and AVIRIS, were used for the study. A brief presentation of the principal component analysis approach is followed by an examination of the information contents of the principal component image bands, which revealed that only the first few bands contain significant information. The use of the first few principal component images can yield about 70 percent correct classification rate. This study suggests the benefit and efficiency of using the principal component analysis technique as a preprocessing step for the classification of' hyperspectral images. Numéro de notice : A2002-192 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22107 [article]