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  • Multiple graph regularized NMF for hyperspectral unmixing

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    TongPUB2673.pdf (475.5Kb)
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    Accepted Manuscript (AM)
    Author(s)
    Tong, Lei
    Zhou, Jun
    Qian, Yuntao
    Gao, Yongsheng
    Griffith University Author(s)
    Gao, Yongsheng
    Zhou, Jun
    Year published
    2015
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    Abstract
    Hyperspectral unmixing is an important technique for estimating fraction of different land covers from remote sensing imagery. In recent years, nonnegative matrix factorization (NMF) methods with various constraints have been introduced into hyperspectral unmixing. Among these methods, graph based constraint has been proved to be useful in capturing the latent manifold structure of the hyper-spectral data in the feature domain. However, due to the complexity of the data, only using single graph can not adequately reflect the intrinsic property of the data. In this paper, we propose a multiple graph regularized NMF method for ...
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    Hyperspectral unmixing is an important technique for estimating fraction of different land covers from remote sensing imagery. In recent years, nonnegative matrix factorization (NMF) methods with various constraints have been introduced into hyperspectral unmixing. Among these methods, graph based constraint has been proved to be useful in capturing the latent manifold structure of the hyper-spectral data in the feature domain. However, due to the complexity of the data, only using single graph can not adequately reflect the intrinsic property of the data. In this paper, we propose a multiple graph regularized NMF method for hyperspectral unmixing, which approximates the manifold and consistency of data by a linear combination of several graphs constructed in different scales. Results on both synthetic and real data have validated the effectiveness of the proposed method, and shown that it has outperformed several state-of-the-arts hyperspectral unmixing methods.
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    Conference Title
    2015 7TH WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING (WHISPERS)
    Volume
    2015-June
    DOI
    https://doi.org/10.1109/WHISPERS.2015.8075468
    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/10072/355892
    Collection
    • Conference outputs

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