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  • Material Based Boundary Detection in Hyperspectral Images

    Author(s)
    Al-Khafaji, Suhad Lateef
    Zia, Ali
    Zhou, Jun
    Liew, Alan Wee-Chung
    Griffith University Author(s)
    Liew, Alan Wee-Chung
    Zhou, Jun
    Year published
    2017
    Metadata
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    Abstract
    Boundary detection in hyperspectral image (HSI) is a challenging task due to high data dimensionality and the that is distributed over the spectral bands. For this reason, there is a dearth of research on boundary detection in HSI. In this paper, we propose a spectral-spatial feature based statistical co-occurrence method for this task. We adopt probability density function (PDF) to estimate the co-occurrence of features at neighboring pixel pairs. Such cooccurrence is rare at the boundary and repeated within a region. To fully explore the material information embedded in HSI, joint spectral-spatial features are extracted ...
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    Boundary detection in hyperspectral image (HSI) is a challenging task due to high data dimensionality and the that is distributed over the spectral bands. For this reason, there is a dearth of research on boundary detection in HSI. In this paper, we propose a spectral-spatial feature based statistical co-occurrence method for this task. We adopt probability density function (PDF) to estimate the co-occurrence of features at neighboring pixel pairs. Such cooccurrence is rare at the boundary and repeated within a region. To fully explore the material information embedded in HSI, joint spectral-spatial features are extracted at each pixel. The PDF values are then used to construct an affinity matrix for all pixels. After that, a spectral clustering algorithm is applied on the affinity matrix to produce boundaries. Our algorithm is evaluated on a dataset of real-world HSIs and compared with two alternative approaches. The results show that the proposed method is very effective in exploring object boundaries from HSI images.
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    Conference Title
    2017 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING - TECHNIQUES AND APPLICATIONS (DICTA)
    Volume
    2017-December
    DOI
    https://doi.org/10.1109/DICTA.2017.8227462
    Subject
    Artificial intelligence
    Publication URI
    http://hdl.handle.net/10072/378008
    Collection
    • Conference outputs

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