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ICE: a statistical approach to identifying endmembers in hyperspectral images

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6 Author(s)
Berman, M. ; Macquarie Univ. Campus, North Ryde, NSW, Australia ; Kiiveri, H. ; Lagerstrom, R. ; Ernst, A.
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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.

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Geoscience and Remote Sensing, IEEE Transactions on  (Volume:42 ,  Issue: 10 )