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

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.

Mots clés: hyperspectral image classification; Hilbert Schmidt independence criterion (HSIC); LASSO; surrogate kernel (SK).; Band selection; class separability measure; feature selection; kernel methods

Domaines: Traitement des images [eess.IV]; Sciences de l'environnement

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, 2017, 55 (4), pp.2385-2398. ⟨10.1109/TGRS.2016.2642479⟩. ⟨hal-01447452v2⟩