Fast SVD With Random Hadamard Projection for Hyperspectral Dimensionality Reduction

Abstract

While data-dependent dimensionality reduction has dominated in many applications of hyperspectral imagery, there is increasing interest in data-independent strategies - such as random projections - due to their promise for reduced computational complexity as well as their demonstrated ability to preserve application-important information. Such random-projection-based dimensionality reduction is investigated in the specific context of supervised hyperspectral classification. Both Hadamard- and Gaussian-based random projections are considered, applied alone as well as incorporated into a fast approximate singular value decomposition (SVD). Experimental results reveal that the proposed Hadamard-based random projection with the fast SVD (FSVD) offers a computationally attractive alternative to not only traditional SVD but also Gaussian-based FSVD for dimensionality reduction in hyperspectral classification.