Centre de documentation
scientifique

Linear spectral mixture analysis via multiple-kernel learning for hyperspectral image classification / Keng-Hao Liu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 4 (April 2015) . - pp 2254 - 2269 Titre : Linear spectral mixture analysis via multiple-kernel learning for hyperspectral image classification Type de document : Article/Communication Auteurs : Keng-Hao Liu, Auteur ; Yen-Yu Lin, Auteur ; Chu-Song Chen, Auteur Année de publication : 2015 Article en page(s) : pp 2254 - 2269 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse linéaire des mélanges spectraux [Termes IGN] apprentissage automatique [Termes IGN] classification automatique [Termes IGN] image hyperspectrale Résumé : (Auteur) Linear spectral mixture analysis (LSMA) has received wide interests for spectral unmixing in the remote sensing community. This paper introduces a framework called multiplekernel learning-based spectral mixture analysis (MKL-SMA) that integrates a newly proposed MKL method into the training process of LSMA. MKL-SMA allows us to adopt a set of nonlinear basis kernels to better characterize the data so that it can enrich the discriminant capability in classification. Because a single kernel is often insufficient to well present all the data characteristics, MKL-SMA has the advantage of providing a broader range of representation flexibilities; it also eases the kernel selection process because the kernel combination parameters can be learned automatically. Unlike most MKL approaches where complex nonlinear optimization problems are involved in their training process, we derived a closed-form solution of the kernel combination parameters in MKL-SMA. Our method is thus efficient for training and easy to implement. The usefulness of MKL-SMA is demonstrated by conducting real hyperspectral image experiments for performance evaluation. Promising results manifest the effectiveness of the proposed MKL-SMA. Numéro de notice : A2015-173 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2358620 Date de publication en ligne : 29/09/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2358620 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75891 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2015041	RAB	Revue	Centre de documentation	En réserve L003	Disponible