Randomized SVD Methods in Hyperspectral Imaging

In this paper, 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 suggest 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.