We propose a superpixel weighted low-rank and sparse unmixing method for sparse unmixing. The proposed method consists of two steps. In the first step, we segment hyperspectral image into superpixels which are defined as the homogeneous regions with different shape and sizes according to the spatial structure. Then, an efficient method is proposed to obtain a spatial weight term using superpixels to capture the spatial structure of hyperspectral data. In the second step, we solve a superpixel guided low-rank and spatially weighted sparse approximation problem in which spatial weight term obtained in the first step is used as a weight term in sparsity promoting norm. This formulation exploits the spatial correlation of the pixels in the hyperspectral image efficiently. The experiments are conducted on simulated and real data sets to show the effectiveness of the proposed method.


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    Titel :

    Superpixel Weighted Low-rank and Sparse Approximation for Hyperspectral Unmixing


    Beteiligte:
    Ince, Taner (Autor:in) / Dundar, Tugcan (Autor:in) / Kacmaz, Seydi (Autor:in) / Karci, Hasari (Autor:in)


    Erscheinungsdatum :

    07.06.2023


    Format / Umfang :

    2051294 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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