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Classification of hyperspectral images by exploiting spectral–spatial information of superpixel via multiple kernels / Leyuan Fang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 12 (December 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 12 (December 2015) . - pp 6663 - 6674 Titre : Classification of hyperspectral images by exploiting spectral–spatial information of superpixel via multiple kernels Type de document : Article/Communication Auteurs : Leyuan Fang, Auteur ; Shutao Li, Auteur ; Wuhui Duan, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 6663 - 6674 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] classification spectrale [Termes IGN] données localisées [Termes IGN] image hyperspectrale [Termes IGN] pixel Résumé : (auteur) For the classification of hyperspectral images (HSIs), this paper presents a novel framework to effectively utilize the spectral-spatial information of superpixels via multiple kernels, which is termed as superpixel-based classification via multiple kernels (SC-MK). In the HSI, each superpixel can be regarded as a shape-adaptive region, which consists of a number of spatial neighboring pixels with very similar spectral characteristics. First, the proposed SC-MK method adopts an oversegmentation algorithm to cluster the HSI into many superpixels. Then, three kernels are separately employed for the utilization of the spectral information, as well as spatial information, within and among superpixels. Finally, the three kernels are combined together and incorporated into a support vector machine classifier. Experimental results on three widely used real HSIs indicate that the proposed SC-MK approach outperforms several well-known classification methods. Numéro de notice : A2015-847 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2445767 Date de publication en ligne : 01/07/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2445767 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79197 [article]

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