An improved spectral reflectance and derivative feature fusion for hyperspectral image classification

In this paper, a new method for improving the classification performance of hyperspectral images with the aid of derivative information is investigated. First, spectral features are filtered and derivatives of different orders at different sampling intervals are computed. Then, the suitable spectral magnitude features and different derivative features are chosen by using segmented principle component analysis feature extraction method with optimal parameters, and are stacked to constitute a new feature cube. Finally, the efficacy of the spectral derivatives in improving the classification performance of the hyperspectral data is testified using support vector machine for AVIRIS hyperspectral data. The experimental results show that the proposed method can improve the classification accuracy compared to the traditional classification techniques with spectral magnitude features even on very small training samples.