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  • L1/2 Sparsity Constrained Nonnegative Matrix Factorization for Hyperspectral Unmixing

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    Author(s)
    Qian, Y
    Jia, S
    Zhou, J
    Antonio, RK
    Griffith University Author(s)
    Zhou, Jun
    Year published
    2010
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    Abstract
    Hyperspectral unmixing is a crucial preprocessing step for material classification and recognition. In the last decade, nonnegative matrix factorization (NMF) and its extensions have been intensively studied to unmix hyperspectral imagery and recover the material end-members. As an important constraint, sparsity has been modeled making use of L1 or L2 regularizers. However, the full additivity constraint of material abundances is often overlooked, hence, limiting the practical efficacy of these methods. In this paper, we extend the NMF algorithm by incorporating the L1/2 sparsity constraint. The L1/2-NMF provides more sparse ...
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    Hyperspectral unmixing is a crucial preprocessing step for material classification and recognition. In the last decade, nonnegative matrix factorization (NMF) and its extensions have been intensively studied to unmix hyperspectral imagery and recover the material end-members. As an important constraint, sparsity has been modeled making use of L1 or L2 regularizers. However, the full additivity constraint of material abundances is often overlooked, hence, limiting the practical efficacy of these methods. In this paper, we extend the NMF algorithm by incorporating the L1/2 sparsity constraint. The L1/2-NMF provides more sparse and accurate results than the other regularizers by considering the end-member additivity constraint explicitly in the optimisation process. Experiments on the synthetic and real hyperspectral data validate the proposed algorithm.
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    Conference Title
    Proceedings - 2010 Digital Image Computing: Techniques and Applications, DICTA 2010
    DOI
    https://doi.org/10.1109/DICTA.2010.82
    Copyright Statement
    © 2010 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/10072/51658
    Collection
    • Conference outputs

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