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  • Structured discriminative nonnegative matrix factorization for hyperspectral unmixing

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    Accepted Manuscript (AM)
    Author(s)
    Li, Xue
    Zhou, Jun
    Tong, Lei
    Yu, Xun
    Guo, Jianhui
    Zhao, Chunxia
    Griffith University Author(s)
    Zhou, Jun
    Yu, Alex
    Tong, Lei
    Year published
    2016
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    Abstract
    Hyperspectral unmixing is an important technique for identifying the constituent spectra and estimating their corresponding fractions in an image. Nonnegative Matrix Factorization (NMF) has recently been widely used for hyperspectral unmixing. However, due to the complex distribution of hyperspectral data, most existing NMF algorithms cannot adequately reflect the intrinsic relationship of the data. In this paper, we propose a novel method, Structured Discriminative Nonnegative Matrix Factorization (SDNMF), to preserve the structural information of hyperspectral data. This is achieved by introducing structured discriminative ...
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    Hyperspectral unmixing is an important technique for identifying the constituent spectra and estimating their corresponding fractions in an image. Nonnegative Matrix Factorization (NMF) has recently been widely used for hyperspectral unmixing. However, due to the complex distribution of hyperspectral data, most existing NMF algorithms cannot adequately reflect the intrinsic relationship of the data. In this paper, we propose a novel method, Structured Discriminative Nonnegative Matrix Factorization (SDNMF), to preserve the structural information of hyperspectral data. This is achieved by introducing structured discriminative regularization terms to model both local affinity and distant repulsion of observed spectral responses. Moreover, considering that the abundances of most materials are sparse, a sparseness constraint is also introduced into SDNMF. Experimental results on both synthetic and real data have validated the effectiveness of the proposed method which achieves better unmixing performance than several alternative approaches.
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    Conference Title
    2016 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
    Volume
    2016-August
    DOI
    https://doi.org/10.1109/ICIP.2016.7532678
    Copyright Statement
    © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Image processing
    Publication URI
    http://hdl.handle.net/10072/124192
    Collection
    • Conference outputs

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