Spectral Magnitude and Spectral Derivative Feature Fusion for Improved Classification of Hyperspectral Images

This paper proposes to increase the classification accuracy of hyperspectral images by fusing spectral magnitude features and spectral derivative features. Principle component analysis (PCA) is used as feature extraction method to reduce the final number of features of the hyperspectral data before feature fusion. PCA is applied separately to magnitude and derivative features to determine significant components of each. Different fusion approaches of the significant components of magnitude features and the significant components of the first as well as second spectral derivatives are evaluated to construct the desired number of final features. Support vector machine (SVM) classification is used for classification of hyperspectral images after feature fusion and it is shown that the proposed approach improves classification accuracy.