Variational Mode Decomposition (VMD) is a recent method and is gaining popularity in the area of signal and image processing. The use of this decomposition technique in hyper spectral image classification is discussed in detail in this paper. The role of VMD as a feature extraction technique is exploited here. The proposed method includes an initial stage of dimensionality reduction so as to reduce the computational complexity. A final stage of recursive filtering is also added to further enhance the results. Results obtained by the proposed method on two hyper spectral image datasets 'Indian Pines and Salinas-A, suggests that VMD is a promising method in the area of image analysis and classification. Quality indices used for experimental analysis include overall accuracy (OA), average accuracy (AA) and kappa coefficient. Notable classification accuracy has been obtained for both the datasets and a final stage of recursive filtering has further improved the results (more than 98% accuracy in the case of Indian Pines).
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L. S. Kiran, Sowmya V., and Dr. Soman K. P., “Enhanced Variational Mode Features for Hyperspectral Image Classification”, Journal of Chemical and Pharmaceutical Sciences, vol. 9, pp. 502-505, 2016.