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Representative multiple Kernel learning for classification in hyperspectral imagery / Y. Gu in IEEE Transactions on geoscience and remote sensing, vol 50 n° 7 Tome 2 (July 2012)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 50 n° 7 Tome 2 (July 2012) . - pp 2852 - 2865 Titre : Representative multiple Kernel learning for classification in hyperspectral imagery Type de document : Article/Communication Auteurs : Y. Gu, Auteur ; C. Wang, Auteur ; D. You, Auteur ; et al., Auteur Année de publication : 2012 Article en page(s) : pp 2852 - 2865 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] image hyperspectrale [Termes IGN] méthode fondée sur le noyau Résumé : (Auteur) Recently, multiple kernel learning (MKL) methods have been developed to improve the flexibility of kernel-based learning machine. The MKL methods generally focus on determining key kernels to be preserved and their significance in optimal kernel combination. Unfortunately, computational demand of finding the optimal combination is prohibitive when the number of training samples and kernels increase rapidly, particularly for hyperspectral remote sensing data. In this paper, we address the MKL for classification in hyperspectral images by extracting the most variation from the space spanned by multiple kernels and propose a representative MKL (RMKL) algorithm. The core idea embedded in the algorithm is to determine the kernels to be preserved and their weights according to statistical significance instead of time-consuming search for optimal kernel combination. The noticeable merits of RMKL consist that it greatly reduces the computational load for searching optimal combination of basis kernels and has no limitation from strict selection of basis kernels like most MKL algorithms do; meanwhile, RMKL keeps excellent properties of MKL in terms of both good classification accuracy and interpretability. Experiments are conducted on different real hyperspectral data, and the corresponding experimental results show that RMKL algorithm provides the best performances to date among several the state-of-the-art algorithms while demonstrating satisfactory computational efficiency. Numéro de notice : A2012-322 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2176341 Date de publication en ligne : 17/01/2012 En ligne : https://doi.org/10.1109/TGRS.2011.2176341 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31768 [article]

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