Bayesian Nonnegative Matrix Factorization with Volume Prior for Unmixing of Hyperspectral Images

Morten Arngren, Mikkel Nørgaard Schmidt, Jan Larsen

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    Abstract

    In hyperspectral image analysis the objective is to unmix a set of acquired pixels into pure spectral signatures (endmembers) and corresponding fractional abundances. The Non-negative Matrix Factorization (NMF) methods have received a lot of attention for this unmixing process. Many of these NMF based unmixing algorithms are based on sparsity regularization encouraging pure spectral endmembers, but this is not optimal for certain applications, such as foods, where abundances are not sparse. The pixels will theoretically lie on a simplex and hence the endmembers can be estimated as the vertices of the smallest enclosing simplex. In this context we present a Bayesian framework employing a volume constraint for the NMF algorithm, where the posterior distribution is numerically sampled from using a Gibbs sampling procedure. We evaluate the method on synthetical and real hyperspectral data of wheat kernels.
    Original languageEnglish
    Title of host publication2009 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2009)
    PublisherIEEE
    Publication date2009
    Pages1-6
    ISBN (Print)9781424449477
    DOIs
    Publication statusPublished - 2009
    Event2009 IEEE International Workshop on Machine Learning for Signal Processing - Grenoble, France
    Duration: 2 Sep 20094 Sep 2009
    http://mlsp2009.conwiz.dk/

    Workshop

    Workshop2009 IEEE International Workshop on Machine Learning for Signal Processing
    CountryFrance
    CityGrenoble
    Period02/09/200904/09/2009
    Internet address

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