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  • Spectral-Spatial Boundary Detection in Hyperspectral Images

    Author(s)
    Al-Khafaji, Suhad Lateef
    Zhou, Jun
    Bai, Xiao
    Qian, Yuntao
    Liew, Alan Wee-Chung
    Griffith University Author(s)
    Zhou, Jun
    Liew, Alan Wee-Chung
    Year published
    2022
    Metadata
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    Abstract
    In this paper, we propose a novel method for boundary detection in close-range hyperspectral images. This method can effectively predict the boundaries of objects of similar colour but different materials. To effectively extract the material information in the image, the spatial distribution of the spectral responses of different materials or endmembers is first estimated by hyperspectral unmixing. The resulting abundance map represents the fraction of each endmember spectra at each pixel. The abundance map is used as a supportive feature such that the spectral signature and the abundance vector for each pixel are fused to ...
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    In this paper, we propose a novel method for boundary detection in close-range hyperspectral images. This method can effectively predict the boundaries of objects of similar colour but different materials. To effectively extract the material information in the image, the spatial distribution of the spectral responses of different materials or endmembers is first estimated by hyperspectral unmixing. The resulting abundance map represents the fraction of each endmember spectra at each pixel. The abundance map is used as a supportive feature such that the spectral signature and the abundance vector for each pixel are fused to form a new spectral feature vector. Then different spectral similarity measures are adopted to construct a sparse spectral-spatial affinity matrix that characterizes the similarity between the spectral feature vectors of neighbouring pixels within a local neighborhood. After that, a spectral clustering method is adopted to produce eigenimages. Finally, the boundary map is constructed from the most informative eigenimages. We created a new HSI dataset and use it to compare the proposed method with four alternative methods, one for hyperspectral image and three for RGB image. The results exhibit that our method outperforms the alternatives and can cope with several scenarios that methods based on colour images cannot handle.
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    Journal Title
    IEEE Transactions on Image Processing
    Volume
    31
    DOI
    https://doi.org/10.1109/TIP.2021.3131942
    Subject
    Image processing
    Artificial intelligence
    Science & Technology
    Computer Science, Artificial Intelligence
    Engineering, Electrical & Electronic
    Publication URI
    http://hdl.handle.net/10072/411550
    Collection
    • Journal articles

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