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  • Superpixel-Based Nonnegative Tensor Factorization for Hyperspectral Unmixing

    Author(s)
    Xiong, Fengchao
    Chen, Jingzhou
    Zhou, Jun
    Qian, Yuntao
    Griffith University Author(s)
    Zhou, Jun
    Year published
    2018
    Metadata
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    Abstract
    Hyperspectral unmixing aims at decomposing a hyperspectral image (HSI) into a number of constituted materials and associated proportions. Recently, nonnegative tensor factorization (NTF) based methods have been proved effective and natural for hyperspectral unmixing owing to their virtue of representing an HSI without any information loss. However, these methods take an HSI as a whole, partly ignoring the local information in distinct local regions. In addition, HSIs are high likely to be disturbed by various noise, making the global information unnecessarily reliable. To alleviate these drawbacks, we propose a superpixel-based ...
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    Hyperspectral unmixing aims at decomposing a hyperspectral image (HSI) into a number of constituted materials and associated proportions. Recently, nonnegative tensor factorization (NTF) based methods have been proved effective and natural for hyperspectral unmixing owing to their virtue of representing an HSI without any information loss. However, these methods take an HSI as a whole, partly ignoring the local information in distinct local regions. In addition, HSIs are high likely to be disturbed by various noise, making the global information unnecessarily reliable. To alleviate these drawbacks, we propose a superpixel-based matrix-vector nonnegative tensor factorization (S-MV-NTF) method for hyperspectral unmixing, where both the global information and local information are taken into consideration. In this method, the HSI is firstly partitioned into numerous superpixels, homogeneous regions with adaptive sizes and compact boundaries, representing the local spatial structure information. Then, such local information is integrated to the tensor factorization to make the pixels lying in the same superpixel share similar abundances. Experimental results on synthetic data and real-world data show that the proposed method dominates the state-of-the-art methods.
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    Conference Title
    IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
    DOI
    https://doi.org/10.1109/IGARSS.2018.8518642
    Subject
    Information and computing sciences
    Science & Technology
    Physical Sciences
    Engineering, Electrical & Electronic
    Geosciences, Multidisciplinary
    Publication URI
    http://hdl.handle.net/10072/402413
    Collection
    • Conference outputs

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