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Decision fusion in kernel-induced spaces for hyperspectral image classification / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 2 (June 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 6 Tome 2 (June 2014) . - pp 3399 - 3411 Titre : Decision fusion in kernel-induced spaces for hyperspectral image classification Type de document : Article/Communication Auteurs : Wei Li, Auteur ; Saurabh Prasad, Auteur ; James E. Fowler, Auteur Année de publication : 2014 Article en page(s) : pp 3399 - 3411 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification [Termes IGN] fusion d'images [Termes IGN] image hyperspectrale Résumé : (Auteur) The one-against-one (OAO) strategy is commonly employed with classifiers-such as support vector machines-which inherently provide binary two-class classification in order to handle multiple classes. This OAO strategy is introduced for the classification of hyperspectral imagery using discriminant analysis within kernel-induced feature spaces, producing a pair of algorithms-kernel discriminant analysis and kernel local Fisher discriminant analysis-for dimensionality reduction, which are followed by a quadratic Gaussian maximum-likelihood-estimation classifier. In the proposed approach, a multiclass problem is broken down into all possible binary classifiers, and various decision-fusion rules are considered for merging results from this classifier ensemble. Experimental results using several hyperspectral data sets demonstrate the benefits of the proposed approach-in addition to improved classification performance, the resulting classifier framework requires reduced memory for estimating kernel matrices. Numéro de notice : A2014-309 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2272760 En ligne : https://doi.org/10.1109/TGRS.2013.2272760 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33212 [article]

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