Open AccessRandomized SVD Methods in Hyperspectral Imaging
Author(s) -
Jiani Zhang,
Jennifer B. Erway,
Xiaofei Hu,
Qiang Zhang,
Robert J. Plemmons
Publication year - 2012
Publication title -
journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.318
H-Index - 25
eISSN - 2090-0155
pISSN - 2090-0147
DOI - 10.1155/2012/409357
Subject(s) - hyperspectral imaging , singular value decomposition , dimensionality reduction , principal component analysis , lossless compression , compressed sensing , random projection , matrix (chemical analysis) , pattern recognition (psychology) , artificial intelligence , robust principal component analysis , mathematics , matrix decomposition , curse of dimensionality , rank (graph theory) , randomized algorithm , computer science , singular value , algorithm , data compression , physics , eigenvalues and eigenvectors , chemistry , combinatorics , chromatography , quantum mechanics
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
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