High Accuracy Hyperspectral Image Classification Based on Empirical Mode Decomposition and Composite Kernel

This paper proposes to use Empirical Mode Decomposition EMD to increase the classification accuracy, of hyperspectral images, EMD is a non linear and adaptive signal decomposition approach and decomposes signals into Intrinsic Mode Functions. MFsg and a final residue. In this paper initially EMD is appllied to each hyperspectral image band and the IMFs corresponding to each hyperspectral image band are obtained. Then the information contained in the first IMFs and secondary IMFs of each band are combined using composite kernels Support vector machine SVMg based classification is used to show, the classification performance of the proposed approach Experimental results show that the SVM classification accuracy can significantly, be improved using the proposed EMD and composite kernel based classification approach