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Proceedings Paper

An orthogonal subspace projection-based for estimation of virtual dimensionality for hyperspectral data exploitation
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Paper Abstract

A recently introduced concept, virtual dimensionality (VD) has been shown promise in many applications of hyperspectral data exploitation. It was originally developed for estimating number of spectrally distinct signal sources. This paper explores utility of the VD from various signal processing perspectives and further investigates four techniques, Gershgorin radius (GR), orthogonal projection subspace (OSP), signal subspace estimation (SSE), Neyman-Pearson detection (NPD), to be used to estimate the VD. In particular, the OSP-based VD estimation technique is new and has several advantages over other methods. In order to evaluate their performance, a comparative study and analysis is conducted via synthetic and real image experiments.

Paper Details

Date Published: 7 May 2007
PDF: 12 pages
Proc. SPIE 6565, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII, 65651A (7 May 2007); doi: 10.1117/12.719543
Show Author Affiliations
Weimin Liu, Univ. of Maryland, Baltimore (United States)
Chao-Cheng Wu, Univ. of Maryland, Baltimore (United States)
Chein-I Chang, Univ. of Maryland, Baltimore (United States)


Published in SPIE Proceedings Vol. 6565:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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