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ICE: a statistical approach to identifying endmembers in hyperspectral images

By: Lagerstrom, R.; Kiiveri, H.; Berman, M.; Huntington, J.F.; Dunne, R.; Ernst, A.;

2004 / IEEE

Description

This item was taken from the IEEE Periodical ' ICE: a statistical approach to identifying endmembers in hyperspectral images ' 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.