Wavelet shrinkage denoising of intrinsic mode functions of hyperspectral image bands for classification with high accuracy

This paper proposes Empirical Mode Decomposition (EMD) followed by wavelet shrinkage denoising in hyperspectral image classification to improve classification accuracy. EMD decomposes signals into several Intrinsic Mode Functions (IMFs) and a final residue. In this paper, firstly, EMD is applied to each hyperspectral image band separately to obtain the IMFs of all image bands. Then, the first IMF of each band is applied to wavelet shrinkage denoising, as this IMF includes all local high spatial frequency components. The sums of lower order IMFs are then used to reconstruct hyperspectral image bands that are used as new features for classification. Support Vector Machine (SVM) based classification is used as classification approach in this paper. Experimental results show the effectiveness of the proposed approach.