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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aswathy C, Sowmya V, Soman K (2015) ADMM based hyperspectral image classification improved by denoising using Legendre Fenchel transformation. Indian J Sci Technol 8(24):1
Bhushan DB, Sowmya V, Manikandan MS, Soman K (2011) An effective pre-processing algorithm for detecting noisy spectral bands in hyperspectral imagery. In: 2011 International symposium on ocean electronics, pp 34–39
Chen S, Donoho D. Basis pursuit, vol 1, pp 41–44
Erichson NB, Brunton SL, Kutz JN (2015) Compressed dynamic mode decomposition for real-time object detection. arXiv preprint arXiv:1512.04205
Fong M (2007) Dimension reduction on hyperspectral images. Univ. California, Los Angeles, CA
Koonsanit K, Jaruskulchai C, Eiumnoh A (2012) Band selection for dimension reduction in hyper spectral image using integrated information gain and principal components analysis technique. Int J Mach Learn Comput 2(3):248
Li Y (2014) Dimension reduction for hyperspectral imaging using laplacian eigenmaps and randomized principal component analysis: midyear re-port
Lodha SP, Kamlapur S (2014) Dimensionality reduction techniques for hyperspectral images. Int J Appl Innov Eng Manag (IJAIEM) 3(10)
Reshma R, Sowmya V, Soman K (2016) Dimensionality reduction using band selection technique for kernel based hyperspectral image classification. Procedia Comput Sci 93:396–402
Rodarmel C, Shan J (2002) Principal component analysis for hyper-spectral image classification. Surv Land Inf Sci 62(2):115
Schmid PJ (2010) Dynamic mode decomposition of numerical and experimental data. J Fluid Mech 656:5–28
Selesnick I (2013) Least squares with examples in signal processing. Connexions 4
Srivatsa S, Ajay A, Chandni C, Sowmya V, Soman K (2016) Application of least square denoising to improve ADMM based hyperspectral image classification. Procedia Comput Sci 93:416–423
Xu L, Li F, Wong A, Clausi DA (2015) Hyperspectral image denoising using a spatial–spectral monte carlo sampling approach. IEEE J Sel Top Appl Earth Obs Remote Sens 8(6):3025–3038
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Megha, P., Sowmya, V., Soman, K.P. (2018). Effect of Dynamic Mode Decomposition-Based Dimension Reduction Technique on Hyperspectral Image Classification. In: Nandi, A., Sujatha, N., Menaka, R., Alex, J. (eds) Computational Signal Processing and Analysis. Lecture Notes in Electrical Engineering, vol 490. Springer, Singapore. https://doi.org/10.1007/978-981-10-8354-9_9
Download citation
DOI: https://doi.org/10.1007/978-981-10-8354-9_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8353-2
Online ISBN: 978-981-10-8354-9
eBook Packages: EngineeringEngineering (R0)