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    This study introduces a novel spectral-spatial graph reasoning network (SSGRN) for hyperspectral image classification (HSIC). The SSGRN adaptively generates graph structures from intermediate features, improving feature extraction for irregularly distributed objects.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Remote Sensing

    Background:

    • Convolutional Neural Networks (CNNs) are prevalent in hyperspectral image classification (HSIC).
    • Traditional CNNs struggle with feature extraction for objects exhibiting irregular spatial distributions.
    • Existing graph convolution methods on spatial topologies have limitations due to fixed graph structures and local perception.

    Purpose of the Study:

    • To develop an adaptive approach for feature extraction in hyperspectral image classification.
    • To overcome the limitations of fixed graph structures and local perceptions in graph-based methods.
    • To introduce a novel spectral-spatial graph reasoning network (SSGRN) for enhanced HSIC performance.

    Main Methods:

    • Adaptive superpixel generation on intermediate features during network training to create homogeneous regions.
    • Construction of graph structures using generated spatial descriptors as nodes.
    • Aggregation of channels to generate spectral descriptors and exploration of channel relationships.
    • Utilizing adjacency matrices derived from all descriptors for global perception in graph convolutions.
    • Integration of spatial and spectral graph reasoning subnetworks to form the SSGRN.

    Main Results:

    • The proposed SSGRN effectively extracts spatial and spectral features by adaptively generating graph structures.
    • Global perception is achieved by considering relationships among all descriptors for adjacency matrix computation.
    • Comprehensive experiments on four public datasets validate the method's competitiveness against state-of-the-art approaches.

    Conclusions:

    • The developed spectral-spatial graph reasoning network (SSGRN) offers a significant advancement in hyperspectral image classification.
    • Adaptive superpixel generation and global graph reasoning enhance the extraction of complex spatial-spectral features.
    • The SSGRN demonstrates superior performance compared to existing graph convolution-based methods for HSIC.