Empirical Mode Decomposition Pre-Process for Higher Accuracy Hyperspectral Image Classification

This paper proposes empirical mode decomposition (EMD) based pre-process to increase classification accuracy of hyperspectral images. EMD is an adaptive and non-linear signal decomposition approach and decomposes the data into intrinsic mode functions (IMFs) and a residue. In this paper, EMD is applied to each hyperspectral image band to obtain IMFs. After EMD is performed to each band, new bands are reconstructed as the sum of higher level IMFs and classification is executed over these new bands. Support vector machine (SVM) is used to show the classification performance of the proposed approach. Experimental results show that, utilization of the first two IMFs significantly increases the classification accuracy compared to applying SVM directly to the original data set.