A theory of least-squares target-specified virtual dimensionality in hyperspectral imagery

Drew Paylor, Chein I. Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

Virtual dimensionality (VD) has received considerable interest in its use of specifying the number of spectrally distinct signatures, denoted by p. So far all techniques are eigen-based approaches which use eigenvalues or eigenvectors to estimate the value of p. However, when eigenvalues are used to estimate VD such as Harsanyi-Farrand-Chang's method or hyperspectral signal subspace identification by minimum error (HySime), there will be no way to find what the spectrally distinct signatures are. On the other hand, if eigenvectors/singular vectors are used to estimate VD such as maximal orthogonal complement algorithm (MOCA), eigenvectors/singular vectors do not represent real signal sources. Most importantly, current available methods used to estimate VD run into two major issues. One is the value of VD being fixed at a constant. The other is a lack of providing a means of finding signal sources of interest. As a matter of fact, the spectrally distinct signatures defined by VD should adapt its value to various target signal sources of interest. For example, the number of endmembers should be different from the number of anomalies. In this paper we develop a second-order statistics approach to determining the value of the VD and the virtual endmember basis.

Original languageEnglish
Title of host publicationSatellite Data Compression, Communications, and Processing X
PublisherSPIE
ISBN (Print)9781628410617
DOIs
Publication statusPublished - 2014
EventSatellite Data Compression, Communications, and Processing X - Baltimore, MD, United States
Duration: 2014 May 82014 May 9

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume9124
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceSatellite Data Compression, Communications, and Processing X
Country/TerritoryUnited States
CityBaltimore, MD
Period14-05-0814-05-09

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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