ICE: A statistical approach to identifying endmembers in hyperspectral images

Mark Berman, Harri Kiiveri, Ryan Lagerstrom, Andreas Ernst, Rob Dunne, Jonathan F. Huntington

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309 Citations (Scopus)


Several of the more important endmember-finding algorithms for hyperspectral data are discussed and some of their shortcomings highlighted. A new algorithm - iterated constrained endmembers (ICE) - which attempts to address these shortcomings is introduced. An example of its use is given. There is also a discussion of the advantages and disadvantages of normalizing spectra before the application of ICE or other endmember-finding algorithms.

Original languageEnglish
Pages (from-to)2085-2095
Number of pages11
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number10
Publication statusPublished - Oct 2004
Externally publishedYes


  • Convex geometry
  • Endmember
  • Hyperspectral
  • Normalization
  • Simplex

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