Local covariance matrices for improved target detection performance

C. E. Caefer, S. R. Rotman

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

4 Scopus citations

Abstract

Our research goals in hyperspectral point target detection have been to develop a methodology for algorithm comparison and to advance point target detection algorithms through the fundamental understanding of spatial/spectral statistics. In this paper, we demonstrate improved target detection performance by making better estimates of the covariance matrix. We develop a new type of local covariance matrix which can be implemented in Principal Component space which shows improved performance based on our metrics.

Original languageEnglish
Title of host publicationWHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing
Subtitle of host publicationEvolution in Remote Sensing
DOIs
StatePublished - 21 Dec 2009
EventWHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing - Grenoble, France
Duration: 26 Aug 200928 Aug 2009

Publication series

NameWHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing

Conference

ConferenceWHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing
Country/TerritoryFrance
CityGrenoble
Period26/08/0928/08/09

Keywords

  • Local covariance matrices
  • Spectral data analysis
  • Target detection

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