A two-stage feature extraction for hyperspectral image data classification

Abstract

In this study, a two-stage feature extraction algorithm cooperated with feature selection is proposed for improving hyperspectral data classification. The first stage feature extraction extracts the features for separating all classes and second stage feature extraction extracts the features for separating individual pair of classes, which cannot be well separated in first stage feature space. Then, feature selection is applied for selecting the best features. Real data experimental result show that the proposed 2-stage feature extraction outperforms single stage feature extraction