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Fast forward feature selection of hyperspectral images for classification with gaussian mixture models / Mathieu Fauvel in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol 8 n° 6 (June 2015)  

Public [article]  inIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing > vol 8 n° 6 (June 2015) . - pp 2824 - 2831 Titre : Fast forward feature selection of hyperspectral images for classification with gaussian mixture models Type de document : Article/Communication Auteurs : Mathieu Fauvel, Auteur ; Clément Dechesne , Auteur ; Anthony Zullo, Auteur ; Frédéric Ferraty, Auteur Année de publication : 2015 Article en page(s) : pp 2824 - 2831 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme de Gauss [Termes IGN] apprentissage automatique [Termes IGN] classificateur [Termes IGN] classification [Termes IGN] extraction de traits caractéristiques [Termes IGN] image hyperspectrale [Termes IGN] itération Résumé : (auteur) A fast forward feature selection algorithm is presented in this paper. It is based on a Gaussian mixture model (GMM) classifier. GMM are used for classifying hyperspectral images. The algorithm selects iteratively spectral features that maximizes an estimation of the classification rate. The estimation is done using the k-fold cross validation (k-CV). In order to perform fast in terms of computing time, an efficient implementation is proposed. First, the GMM can be updated when the estimation of the classification rate is computed, rather than re-estimate the full model. Secondly, using marginalization of the GMM, submodels can be directly obtained from the full model learned with all the spectral features. Experimental results for two real hyperspectral data sets show that the method performs very well in terms of classification accuracy and processing time. Furthermore, the extracted model contains very few spectral channels. Numéro de notice : A2015--068 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/JSTARS.2015.2441771 En ligne : http://dx.doi.org/10.1109/JSTARS.2015.2441771 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83227 [article]