AN ICA BASED APPROACH TO HYPERSPECTRAL IMAGE FEATURE REDUCTION

This article proposes a feature reduction technique for hyperspectral images using Independent Component Analysis (ICA). The proposed technique aims at extracting the best subset of class-informative independent components (ICs) for hyperspectral supervised classification. The selection of the most representative components is assured by the minimization of the reconstruction error, which is computed on the training samples used for the supervised classification. The searching strategy is optimized by exploiting a genetic algorithm-based approach where the fitness function is the classification accuracy obtained by using a support vector machine (SVM) classifier. The obtained results show the effectiveness of the proposed approach in providing class-informative components to improve the classification accuracy.