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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

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.

Original languageEnglish
Title of host publicationAlgorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII
DOIs
StatePublished - 2007
EventAlgorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII - Orlando, FL, United States
Duration: 9 Apr 200712 Sep 2007

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume6565
ISSN (Print)0277-786X

Conference

ConferenceAlgorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII
Country/TerritoryUnited States
CityOrlando, FL
Period9/04/0712/09/07

Keywords

  • Gershgorin radius (GR)
  • Neyman-Pearson detection (NPD)
  • Orthogonal projection subspace (OSP)
  • Signal subspace estimation (SSE)
  • Virtual dimensionality (VD)

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