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    ICE: A new method for the multivariate curve resolution of hyperspectral images

    Access Status
    Fulltext not available
    Authors
    Berman, M.
    Phatak, Aloke
    Lagerstrom, R.
    Wood, B.
    Date
    2009
    Type
    Journal Article
    
    Metadata
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    Citation
    Berman, 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.
    Source Title
    Journal of Chemometrics
    DOI
    10.1002/cem.1198
    ISSN
    0886-9383
    School
    Department of Mathematics and Statistics
    URI
    http://hdl.handle.net/20.500.11937/26356
    Collection
    • Curtin Research Publications
    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.

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