  Convolutional neural networks have been widely applied to hyperspectral image
classification. However, traditional convolutions can not effectively extract
features for objects with irregular distributions. Recent methods attempt to
address this issue by performing graph convolutions on spatial topologies, but
fixed graph structures and local perceptions limit their performances. To
tackle these problems, in this paper, different from previous approaches, we
perform the superpixel generation on intermediate features during network
training to adaptively produce homogeneous regions, obtain graph structures,
and further generate spatial descriptors, which are served as graph nodes.
Besides spatial objects, we also explore the graph relationships between
channels by reasonably aggregating channels to generate spectral descriptors.
The adjacent matrices in these graph convolutions are obtained by considering
the relationships among all descriptors to realize global perceptions. By
combining the extracted spatial and spectral graph features, we finally obtain
a spectral-spatial graph reasoning network (SSGRN). The spatial and spectral
parts of SSGRN are separately called spatial and spectral graph reasoning
subnetworks. Comprehensive experiments on four public datasets demonstrate the
competitiveness of the proposed methods compared with other state-of-the-art
graph convolution-based approaches.
