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Sparse and low-rank graph for discriminant analysis of hyperspectral imagery / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 7 (July 2016) . - pp 4094 - 4105 Titre : Sparse and low-rank graph for discriminant analysis of hyperspectral imagery Type de document : Article/Communication Auteurs : Wei Li, Auteur ; Jiabin Liu, Auteur ; Qian Du, Auteur Année de publication : 2016 Article en page(s) : pp 4094 - 4105 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse discriminante [Termes IGN] analyse en composantes principales [Termes IGN] graphe [Termes IGN] image hyperspectrale [Termes IGN] méthode fondée sur le noyau [Termes IGN] valeur propre Résumé : (Auteur) Recently, sparse graph-based discriminant analysis (SGDA) has been developed for the dimensionality reduction and classification of hyperspectral imagery. In SGDA, a graph is constructed by ℓ1-norm optimization based on available labeled samples. Different from traditional methods (e.g., k-nearest neighbor with Euclidean distance), weights in an ℓ1-graph derived via a sparse representation can automatically select more discriminative neighbors in the feature space. However, the sparsity-based graph represents each sample individually, lacking a global constraint on each specific solution. As a consequence, SGDA may be ineffective in capturing the global structures of data. To overcome this drawback, a sparse and low-rank graph-based discriminant analysis (SLGDA) is proposed. Low-rank representation has been proved to be capable of preserving global data structures, although it may result in a dense graph. In SLGDA, a more informative graph is constructed by combining both sparsity and low rankness to maintain global and local structures simultaneously. Experimental results on several different multiple-class hyperspectral-classification tasks demonstrate that the proposed SLGDA significantly outperforms the state-of-the-art SGDA. Numéro de notice : A2016-879 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2536685 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2536685 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83042 [article]