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

Mark Berman, Aloke Phatak, Ryan Lagerstrom, Bayden R. Wood

    Research output: Contribution to journalArticlepeer-review

    24 Citations (Scopus)

    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.
    Original languageEnglish
    Pages (from-to)101-116
    Number of pages16
    JournalJournal of Chemometrics
    Volume23
    Issue number2
    DOIs
    Publication statusPublished - 2009

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