Hyperspectral image classification with spectral gradient enhancement for empirical mode decomposition

This paper proposes an empirical mode decomposition (EMD) based approach with spectral gradient enhancement for hyperspectral image classification using support vector machines (SVM). In a previous study, it has been shown that using the sum of intrinsic mode functions (IMFs), obtained by applying two-dimensional (2D) EMD to each hyperspectral band, increases the classification accuracies significantly. In this paper, it is shown that using optimum weights for the IMFs, instead of the equal weight approach of the previous study, results in increased classification accuracies. The weights for the IMFs are obtained by a genetic algorithm (GA) based optimization strategy which aims to maximize spectral gradient and hence incorporate spectral processing with the spatial processing of 2D EMD.