Fusion of multispectral and hyperspectral images based on sparse representation

Abstract : This paper presents an algorithm based on sparse representation for fusing hyperspectral and multispectral images. The observed images are assumed to be obtained by spectral or spatial degradations of the high resolution hyperspectral image to be recovered. Based on this forward model, the fusion process is formulated as an inverse problem whose solution is determined by optimizing an appropriate criterion. To incorporate additional spatial information within the objective criterion, a regularization term is carefully designed,relying on a sparse decomposition of the scene on a set of dictionaryies. The dictionaries and the corresponding supports of active coding coef�cients are learned from the observed images. Then, conditionally on these dictionaries and supports, the fusion problem is solved by iteratively optimizing with respect to the target image (using the alternating direction method of multipliers) and the coding coefcients. Simulation results demonstrate the ef�ciency of the proposed fusion method when compared with the state-of-the-art.
Type de document :
Communication dans un congrès
22nd European Signal and Image Processing Conference (EUSIPCO 2014), Sep 2014, Lisbon, Portugal. pp. 1577-1581, 2014
Liste complète des métadonnées

Littérature citée [26 références]  Voir  Masquer  Télécharger

https://hal.archives-ouvertes.fr/hal-01178562
Contributeur : Open Archive Toulouse Archive Ouverte (oatao) <>
Soumis le : lundi 20 juillet 2015 - 13:41:59
Dernière modification le : mercredi 12 septembre 2018 - 17:46:02
Document(s) archivé(s) le : mercredi 21 octobre 2015 - 10:49:13

Fichier

wei_13087.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01178562, version 1
  • OATAO : 13087

Collections

Citation

Qi Wei, José M. Bioucas-Dias, Nicolas Dobigeon, Jean-Yves Tourneret. Fusion of multispectral and hyperspectral images based on sparse representation. 22nd European Signal and Image Processing Conference (EUSIPCO 2014), Sep 2014, Lisbon, Portugal. pp. 1577-1581, 2014. 〈hal-01178562〉

Partager

Métriques

Consultations de la notice

146

Téléchargements de fichiers

72