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Publication Type : Conference Paper
Publisher : Lecture Notes in Electrical Engineering
Source : Lecture Notes in Electrical Engineering, Springer Verlag, Volume 490, p.89-99 (2018)
ISBN : 9789811083532
Keywords : Alternating direction method of multipliers, Classification accuracy, De-noising, Dimension reduction, Dimension reduction algorithm, Dimension reduction techniques, Dynamic mode decompositions, High dimensions, Hyperspectral imaging, image classification, Independent component analysis, Least squares approximations, Principal component analysis, Remote sensing, Signal to noise ratio, Singular value decomposition, Spectroscopy
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Year : 2018
Abstract : Hyperspectral imaging has become an interesting area of research in remote sensing over the past thirty years. But the main hurdles in understanding and analyzing hyperspectral datasets are the high dimension and presence of noisy bands. This work proposes a dynamic mode decomposition (DMD)-based dimension reduction technique for hyperspectral images. The preliminary step is to denoise every band in a hyperspectral image using least square denoising, and the second stage is to apply DMD on hyperspectral images. In the third stage, the denoised and dimension reduced data is given to alternating direction method of multipliers (ADMMs) classifier for validation. The effectiveness of proposed method in selecting most informative bands is compared with standard dimension reduction algorithms like principal component analysis (PCA) and singular value decomposition (SVD) based on classification accuracies and signal-to-noise ratio (SNR). The results illuminate that the proposed DMD-based dimension reduction technique is comparable with the other dimension reduction algorithms in reducing redundancy in band information. © Springer Nature Singapore Pte Ltd. 2018.
Cite this Research Publication : P. Megha, Sowmya, and Dr. Soman K. P., “Effect of dynamic mode decomposition-based dimension reduction technique on hyperspectral image classification”, in Lecture Notes in Electrical Engineering, 2018, vol. 490, pp. 89-99.