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  • Tensor morphological profile for hyperspectral image classification

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    LiangPUB1812.pdf (1.385Mb)
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    Accepted Manuscript (AM)
    Author(s)
    Liang, Jie
    Zhou, Jun
    Gao, Yongsheng
    Griffith University Author(s)
    Gao, Yongsheng
    Zhou, Jun
    Year published
    2016
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    Abstract
    This paper proposes a novel multi-dimensional morphology descriptor, tensor morphology profile (TMP), for hyperspectral image classification. TMP is a general framework to extract the multi-dimensional structures in high-dimensional data. The nth-order morphology profile is proposed to work with the nth-order tensor, which can capture the inner high order structures. This is different with the traditional mathematical morphology operations which are usually limited to two-dimensional data. By treating hyperspectral images a tensor, it is possible to extend the morphology to high dimensional data so that the powerful morphological ...
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    This paper proposes a novel multi-dimensional morphology descriptor, tensor morphology profile (TMP), for hyperspectral image classification. TMP is a general framework to extract the multi-dimensional structures in high-dimensional data. The nth-order morphology profile is proposed to work with the nth-order tensor, which can capture the inner high order structures. This is different with the traditional mathematical morphology operations which are usually limited to two-dimensional data. By treating hyperspectral images a tensor, it is possible to extend the morphology to high dimensional data so that the powerful morphological tools can be used to analyze the hyperspectral images with spectral-spatial information fused. Experimental results on two commonly used hyperspectral images show that the tensor morphological profile consistently performs better than the extended morphological profile for hyperspectral image classification.
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    Conference Title
    2016 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
    Volume
    2016-August
    DOI
    https://doi.org/10.1109/ICIP.2016.7532748
    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/10072/124191
    Collection
    • Conference outputs

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