Segmentation of hyperspectral images based on histograms of principal components

J. Silverman, S. R. Rotman, C. E. Caefer

Research output: Contribution to journalConference articlepeer-review

21 Scopus citations


Further refinements are presented on a simple and fast way to cluster/segment hyperspectral imagery. In earlier work, it was shown that, starting with the first 2 principal component images, one could form a 2-dimensional histogram and cluster all pixels on the basis of the proximity to the peaks. Issues that we analyzed this year are the proper weighting of the different principal components as a function of the peak shape and automatic methods based on an entropy measure to control the number of clusters and the segmentation of the data to produce the most meaningful results. Examples from both visible and infrared hyperspectral data will be shown.

Original languageEnglish
Pages (from-to)270-277
Number of pages8
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


  • Entropy
  • Histograms
  • Hyperspectral
  • Principal components
  • Segmentation

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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