Advances in Spectral-Spatial Classification of Hyperspectral Images

Abstract : Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes character- istics about the size, orientation, and contrast of the spatial structures present in the image. Then, the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines (SVMs) using the available spectral information and the extracted spatial information. Spatial postprocessing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple-classifier (MC) system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral-spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.
Type de document :
Article dans une revue
Proceedings of the IEEE, Institute of Electrical and Electronics Engineers, 2013, 101 (3), pp.652-675. 〈10.1109/JPROC.2012.2197589〉
Liste complète des métadonnées

Littérature citée [115 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-00737075
Contributeur : Yuliya Tarabalka <>
Soumis le : lundi 1 octobre 2012 - 11:29:54
Dernière modification le : mercredi 19 septembre 2018 - 01:14:59
Document(s) archivé(s) le : mercredi 2 janvier 2013 - 05:05:12

Fichier

2012_Fauvel.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Citation

Mathieu Fauvel, Yuliya Tarabalka, Jon Atli Benediktsson, Jocelyn Chanussot, James Tilton. Advances in Spectral-Spatial Classification of Hyperspectral Images. Proceedings of the IEEE, Institute of Electrical and Electronics Engineers, 2013, 101 (3), pp.652-675. 〈10.1109/JPROC.2012.2197589〉. 〈hal-00737075〉

Partager

Métriques

Consultations de la notice

825

Téléchargements de fichiers

4137