Morphological scale-space for hyperspectral images and dimensionality exploration using tensor modeling

Abstract : This paper proposes a framework to integrate spatial information into unsupervised feature extraction for hyperspectral images. In this approach a nonlinear scale-space representation using morphological levelings is formulated. In order to apply feature extraction, tensor principal components are computed involving spatial and spectral information. The proposed method has shown significant gain over the conventional schemes used with real hyperspectral images. In addition, the proposed framework opens a wide field for future developments in which spatial information can be easily integrated into the feature extraction stage. Examples using real hyperspectral images with high spatial resolution showed excellent performance even with a low number of training samples.
Type de document :
Communication dans un congrès
Hyperspectral Image and Signal Processing: Evolution in Remote Sensing:, Aug 2009, Grenoble, France. pp.1 - 4, 2009, 〈10.1109/WHISPERS.2009.5289059〉
Liste complète des métadonnées

Littérature citée [9 références]  Voir  Masquer  Télécharger

https://hal-mines-paristech.archives-ouvertes.fr/hal-00458687
Contributeur : Santiago Velasco-Forero <>
Soumis le : lundi 22 février 2010 - 10:21:16
Dernière modification le : vendredi 27 octobre 2017 - 17:36:02
Document(s) archivé(s) le : vendredi 18 juin 2010 - 21:35:41

Fichier

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

Identifiants

Collections

Citation

Santiago Velasco-Forero, Jesus Angulo. Morphological scale-space for hyperspectral images and dimensionality exploration using tensor modeling. Hyperspectral Image and Signal Processing: Evolution in Remote Sensing:, Aug 2009, Grenoble, France. pp.1 - 4, 2009, 〈10.1109/WHISPERS.2009.5289059〉. 〈hal-00458687〉

Partager

Métriques

Consultations de
la notice

138

Téléchargements du document

90