Fast SVD With Random Hadamard Projection for Hyperspectral Dimensionality Reduction

• Published in: IEEE geoscience and remote sensing letters Vol. 13; no. 9; pp. 1275 - 1279
• Main Authors: Menon, Vineetha, Qian Du, Fowler, James E.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Approximation; Classification; Computation; Computational efficiency; Dimensionality reduction; hyperspectral imagery; Hyperspectral imaging; Preserves; Prognostics and health management; Projection; random projection; Reduction; Remote sensing; Singular value decomposition; supervised classification; Support vector machines; Testing; Training data
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2016.2581172

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.