Algorithms for point target detection in hyperspectral imagery

C. E. Caefer, S. R. Rotman, J. Silverman, P. W. Yip

Research output: Contribution to journalConference articlepeer-review

26 Scopus citations


Two techniques for detecting point targets in hyperspectral imagery are described. The first technique is based on the principal component analysis of hyperspectral data. We combine the information of the first two principal component analysis images; the result is a single image display "summary" of the data cube. The summary frame is used to define image segments. The statistics, means and variances, of each segment for the principal component images is calculated and a covariance matrix is constructed. The local pixel statistics and the segment statistics are then used to evaluate the extent to which each pixel differs from its surroundings. Point target pixels will have abnormally high values. The second technique operates on each band of the hypercube. A local anti-median of each pixel is taken and is weighted by the standard deviation of the local neighborhood. The results of each band are then combined. Results will be shown for visible, SWIR, and MWIR hyperspectral imagery.

Original languageEnglish
Pages (from-to)242-257
Number of pages16
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 1 Dec 2002
EventImaging Spectrometry VII - Seattle, WA, United States
Duration: 8 Jul 200210 Jul 2002


  • Hyperspectral data
  • Point target detection

ASJC Scopus subject areas

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


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