Segmentation of multi-dimensional infrared imagery from histograms

Jerry Silverman, Stanley R. Rotman, Charlene E. Caefer

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


We present a technique for segmenting multi-dimensional data cubes based on multi-dimensional histograms. The histograms are formed from single gray-scale image reductions of the data cube such as principal component images. A segmentation is effected by associating each pixel with one of the peaks in the histogram. No spatial constraints are imposed and no training pixels are required. The following refinements to this simple process are described: proper weighting of the different principal components as a function of the peak shape; and automatic methods based on an entropy measure to generate a reasonable segmentation at a specified number of levels. Examples from both visible and infrared hyperspectral data will be shown.

Original languageEnglish
Pages (from-to)191-200
Number of pages10
JournalInfrared Physics and Technology
Issue number3
StatePublished - 1 May 2004


  • Entropy
  • Gaussian model
  • Histograms
  • Hyperspectral
  • Principal Components
  • Segmentation

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Condensed Matter Physics


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