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Publication Type : Journal Article
Publisher : Signal, Image and Video Processing
Source : Signal, Image and Video Processing, Springer-Verlag, part of Springer Nature 2018, Volume 12, Issue 8, London Ltd, p.1463-1470 (IF: 1.794, CiteScore: 3.8, Q2- 69 percentile) (2018)
Keywords : Dimensionality reduction, Hyperspectral images, Sparse hyperspectral unmixing, Variational mode decomposition (VMD)
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Verified : No
Year : 2018
Abstract : In this work, a frequency-based dimensionality reduction technique using variational mode decomposition (VMD) is proposed. Dimensionality reduction is a very important aspect of preprocessing in case of hyperspectral image (HSI) analysis where this step helps in elimination of the lesser informative bands, thereby reducing the size of the data and making its processing computationally less challenging. In contrast to the standard dimensionality reduction methods such as inter-band block correlation (IBBC) where bands are eliminated based on their similarity with the consecutive bands, the proposed method uses frequency information of each band to categorize it as a less or more informative band. In this way, only the topmost informative bands of HSI are selected to form the reduced dataset. In our experiment, in order to verify the efficiency of VMD as a dimensionality reduction technique, the hyperspectral unmixed results obtained for IBBC reduced dataset is compared with those obtained for VMD reduced dataset. From the parametric measures such as classification accuracy, root-mean-square error (RMSE) and visual results obtained after unmixing for both IBBC and VMD reduced datasets, it is noticed that the VMD reduced dataset performs better by achieving higher classification accuracy and lower RMSE than that of the existing IBBC method.
Cite this Research Publication : M. Swarna, Sowmya V., and Dr. Soman K. P., “Band selection using variational mode decomposition applied in sparsity-based hyperspectral unmixing algorithms”, Signal, Image and Video Processing, vol. 12, no. 8, pp. 1463-1470 (IF: 1.794, CiteScore: 3.8, Q2- 69 percentile), 2018.