Spatially variant dimensionality reduction for the visualization of multi/hyperspectral images

Abstract : In this paper, we introduce a new approach for color visu- alization of multi/hyperspectral images. Unlike traditional methods, we propose to operate a local analysis instead of considering that all the pixels are part of the same population. It takes a segmentation map as an input and then achieves a dimensionality reduction adaptively inside each class of pixels. Moreover, in order to avoid unappealing discon- tinuities between regions, we propose to make use of a set of distance transform maps to weigh the mapping applied to each pixel with regard to its relative location with classes' centroids. Results on two hyperspec- tral datasets illustrate the efficiency of the proposed method.
Type de document :
Communication dans un congrès
International Conference on Image Analysis and Recognition, Jun 2011, Burnaby, Canada. 6753/2011, pp.375-384, 2011, Lecture Notes on Computer Science. 〈10.1007/978-3-642-21593-3_38〉
Liste complète des métadonnées

Littérature citée [11 références]  Voir  Masquer  Télécharger

https://hal-univ-bourgogne.archives-ouvertes.fr/hal-00637936
Contributeur : Alamin Mansouri <>
Soumis le : jeudi 3 novembre 2011 - 12:12:26
Dernière modification le : jeudi 3 novembre 2011 - 14:09:34
Document(s) archivé(s) le : samedi 4 février 2012 - 02:27:15

Fichier

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

Identifiants

Collections

Citation

Steven Le Moan, Alamin Mansouri, Yvon Voisin, Jon Hardeberg. Spatially variant dimensionality reduction for the visualization of multi/hyperspectral images. International Conference on Image Analysis and Recognition, Jun 2011, Burnaby, Canada. 6753/2011, pp.375-384, 2011, Lecture Notes on Computer Science. 〈10.1007/978-3-642-21593-3_38〉. 〈hal-00637936〉

Partager

Métriques

Consultations de
la notice

226

Téléchargements du document

83