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A Comparative Analysis of Variational Mode and Empirical Mode Features on Hyperspectral Image Classification

Publication Type : Journal Article

Publisher : Rajagiri School of Social Sciences, Kalamassery, Kochi

Source : International Journal of Applied Engineering Research, International Journal of Applied Engineering and Research ( IJAER), Volume 10, Issue 73, Department of Computer Science, Rajagiri School of Social Sciences, Kalamassery, Kochi (2015)

Campus : Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking

Department : Electronics and Communication

Year : 2015

Abstract : Considering the fact that involving spatial information in feature extraction significantly improves the classification accuracies, this paper focuses on Variational Mode Decomposition (VMD) and Empirical Mode Decomposition (EMD) as the featureextraction algorithms. Both the algorithms decompose an input image into different modes with each mode including different regions of frequency with unique properties. Here, the proposed method includes processing the same set of data with two different decomposition methods to compare the effect of the methods on the standard dataset. The method incorporates a preprocessing technique for noisy band removal, processing technique for feature extraction, band selection methods for dimensionality reduction and classification technique for result validation

Cite this Research Publication : N. Nechikkat, Sowmya, and Dr. Soman K. P., “A Comparative Analysis of Variational Mode and Empirical Mode Features on Hyperspectral Image Classification”, International Journal of Applied Engineering Research, vol. 10, no. 73, 2015.

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