Empirical Mode Decomposition of Hyperspectral Images for Support Vector Machine Classification

Abstract

This paper presents the utilization of empirical mode decomposition (EMD) of hyperspectral images to increase the classification accuracy using support vector machine (SVM)-based classification. EMD has been shown in the literature to be particularly suitable for nonlinear and nonstationary signals and is used in this paper to decompose hyperspectral image bands into several intrinsic mode functions (IMFs) and a final residue. EMD is utilized in this paper to improve hyperspectral-image-classification accuracy by effectively exploiting the feature that EMD performs a decomposition that is spatially adaptive with respect to intrinsic features. This paper presents two different approaches for improved hyperspectral image classification making use of EMD. In the first approach, IMFs corresponding to each hyperspectral image band are obtained and the sums of lower order IMFs are used as new features for classification with SVM. In the second approach, the pieces of information contained in the first and second IMFs of each hyperspectral image band are combined using composite kernels for SVM classification with higher accuracy.