Randomized SVD methods in hyperspectral imaging

Authors:
Jiani Zhang;Jennifer Erway;Xiaofei Hu;Qiang Zhang;Robert Plemmons
Affiliations:
Department of Mathematics, Wake Forest University, Winston-Salem, NC;Department of Mathematics, Wake Forest University, Winston-Salem, NC;Department of Mathematics, Wake Forest University, Winston-Salem, NC;Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC;Departments of Mathematics and Computer Science, Wake Forest University, Winston-Salem, NC
Venue:
Journal of Electrical and Computer Engineering - Special issue on Algorithms for Multispectral and Hyperspectral Image Analysis
Year:
2012

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Abstract

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 aremade 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.