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Journal of Electrical and Computer Engineering
Volume 2012, Article ID 409357, 15 pages
Research Article

Randomized SVD Methods in Hyperspectral Imaging

1Department of Mathematics, Wake Forest University, Winston-Salem, NC 27109, USA
2Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
3Departments of Mathematics and Computer Science, Wake Forest University, Winston-Salem, NC 27109, USA

Received 16 May 2012; Accepted 2 August 2012

Academic Editor: Heesung Kwon

Copyright © 2012 Jiani Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We present a randomized singular value decomposition (rSVD) method for the purposes of lossless compression, reconstruction, classification, and target detection with hyperspectral (HSI) data. Recent work in low-rank matrix approximations obtained from random projections suggests that these approximations are well suited for randomized dimensionality reduction. Approximation errors for the rSVD are evaluated on HSI, and comparisons are made to deterministic techniques and as well as to other randomized low-rank matrix approximation methods involving compressive principal component analysis. Numerical tests on real HSI data suggest that the method is promising and is particularly effective for HSI data interrogation.