Sparse Hilbert Schmidt Independence Criterion and Surrogate-Kernel-Based Feature Selection for Hyperspectral Image Classification

Bharath Bhushan Damodaran 1 Nicolas Courty 1 Sébastien Lefèvre 1
1 OBELIX - Environment observation with complex imagery
UBS - Université de Bretagne Sud, IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : Designing an effective criterion to select a subset of features is a challenging problem for hyperspectral image classification. In this paper, we develop a feature selection method to select a subset of class discriminant features for hyperspectral image classification. First, we propose a new class separability measure based on the surrogate kernel and Hilbert Schmidt independence criterion in the reproducing kernel Hilbert space. Second, we employ the proposed class separability measure as an objective function and we model the feature selection problem as a continuous optimization problem using LASSO optimization framework. The combination of the class separability measure and the LASSO model allows selecting the subset of features that increases the class separability information and also avoids a computationally intensive subset search strategy. Experiments conducted with three hyperspectral data sets and different experimental settings show that our proposed method increases the classification accuracy and outperforms the state-of-the-art methods.
Type de document :
Article dans une revue
IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2017, <10.1109/TGRS.2016.2642479>
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01447452
Contributeur : Bharath Bhushan Damodaran <>
Soumis le : lundi 27 mars 2017 - 10:46:14
Dernière modification le : mercredi 5 avril 2017 - 09:20:00

Fichier

HSIC-FS.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Citation

Bharath Bhushan Damodaran, Nicolas Courty, Sébastien Lefèvre. Sparse Hilbert Schmidt Independence Criterion and Surrogate-Kernel-Based Feature Selection for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2017, <10.1109/TGRS.2016.2642479>. <hal-01447452v2>

Partager

Métriques

Consultations de
la notice

144

Téléchargements du document

50