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dc.contributor.authorBerman, M.
dc.contributor.authorPhatak, Aloke
dc.contributor.authorLagerstrom, R.
dc.contributor.authorWood, B.
dc.date.accessioned2017-01-30T12:53:05Z
dc.date.available2017-01-30T12:53:05Z
dc.date.created2016-09-12T08:36:44Z
dc.date.issued2009
dc.identifier.citationBerman, M. and Phatak, A. and Lagerstrom, R. and Wood, B. 2009. ICE: A new method for the multivariate curve resolution of hyperspectral images. Journal of Chemometrics. 23 (2): pp. 101-116.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/26356
dc.identifier.doi10.1002/cem.1198
dc.description.abstract

The iterated constrained endmembers (ICE) algorithm is a new method of unmixing hyperspectral images that combines aspects of multivariate curve resolution (MCR) methods in chemometrics and unmixing algorithms in remote sensing. Like many MCR methods, ICE also estimates pure components, or endmembers, via alternating least squares; however, it is explicitly based on a convex geometry model and estimation is carried out in a subspace of reduced dimensionality defined by the minimum noise fraction (MNF) transform. In this paper, we describe the ICE algorithm and its properties. We also illustrate its use on a hyperspectral image of cervical tissue. The unmixing of hyperspectral images presents some unique challenges, and we also outline where further development is required. Copyright © 2008 John Wiley & Sons, Ltd.

dc.titleICE: A new method for the multivariate curve resolution of hyperspectral images
dc.typeJournal Article
dcterms.source.volume23
dcterms.source.number2
dcterms.source.startPage101
dcterms.source.endPage116
dcterms.source.issn0886-9383
dcterms.source.titleJournal of Chemometrics
curtin.departmentDepartment of Mathematics and Statistics
curtin.accessStatusFulltext not available


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