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

Weimin Liu; Chao-Cheng Wu; Chein-I Chang

doi:10.1117/12.719543

7 May 2007 An orthogonal subspace projection-based for estimation of virtual dimensionality for hyperspectral data exploitation

Weimin Liu, Chao-Cheng Wu, Chein-I Chang

Author Affiliations +

Proceedings Volume 6565, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII; 65651A (2007) https://doi.org/10.1117/12.719543
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

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.

Citation Download Citation

Weimin Liu, Chao-Cheng Wu, and Chein-I Chang "An orthogonal subspace projection-based for estimation of virtual dimensionality for hyperspectral data exploitation", Proc. SPIE 6565, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII, 65651A (7 May 2007); https://doi.org/10.1117/12.719543

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