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  • Multilayer manifold and sparsity constrainted nonnegative matrix factorization for hyperspectral unmixing

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    Accepted Manuscript (AM)
    Author(s)
    Shu, Zhenqiu
    Zhou, Jun
    Tong, Lei
    Bai, Xiao
    Zhao, Chunxia
    Griffith University Author(s)
    Zhou, Jun
    Tong, Lei
    Year published
    2015
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    Abstract
    Given a hyperspectral image, unmixing tries to estimate the spectral responses of the latent constituent materials and their corresponding fractions. Recently, Nonnegative Matrix Factorization (NMF) has been widely applied to solve the hyper-spectral unmixing problem because of its plausible physical interpretation. In this paper, we propose a novel method, Multilayer Manifold and Sparsity constrained Nonnegative Matrix Factorization (MMSNMF), for hyperspectral unmixing. In this approach, Multilayer NMF decomposes a hyperspectral image iteratively at several layers. In order to consider both the manifold structure of ...
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    Given a hyperspectral image, unmixing tries to estimate the spectral responses of the latent constituent materials and their corresponding fractions. Recently, Nonnegative Matrix Factorization (NMF) has been widely applied to solve the hyper-spectral unmixing problem because of its plausible physical interpretation. In this paper, we propose a novel method, Multilayer Manifold and Sparsity constrained Nonnegative Matrix Factorization (MMSNMF), for hyperspectral unmixing. In this approach, Multilayer NMF decomposes a hyperspectral image iteratively at several layers. In order to consider both the manifold structure of hyperspectral image and the sparsity of abundance matrix, we impose a graph regularization term and a sparsity regularization term on both the spectral signature matrix and the abundance matrix. Experimental results on both synthetic and real data validate the effectiveness of the proposed method in hyperspectral unmixing.
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    Conference Title
    2015 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
    Volume
    2015-December
    DOI
    https://doi.org/10.1109/ICIP.2015.7351186
    Copyright Statement
    © 2015 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/10072/169474
    Collection
    • Conference outputs

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