An empirical mode decomposition and composite kernel approach to increase hyperspectral image classification accuracy

This paper proposes to increase the classification accuracy of hyperspectral images based on Empirical Mode Decomposition (EMD) algorithm and composite kernels. EMD is a signal decomposition algorithm and decomposes signals into several Intrinsic Mode Functions (IMFs) and a final residue. In this paper, two-dimensional EMD is initially applied to each hyperspectral image band separately and IMFs of hyperspectral image bands are obtained. Composite kernels are used to combine the information contained in the first IMFs and second IMFs of all bands and kernel based Support Vector Machine (SVM) is used for classification. Experimental results confirm the usefulness of the proposed approach compared to direct SVM approach.