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.

翻译：卷积神经网络已被广泛应用于高光谱图像分类。然而，传统卷积无法有效提取具有不规则分布目标的特征。近期方法试图通过在图拓扑上执行图卷积来解决该问题，但固定的图结构与局部感知能力限制了其性能。针对这些问题，本文提出一种区别于先前方法的新思路：在网络训练过程中对中间特征进行超像素生成，以自适应地产生均匀区域、获取图结构，并进一步生成作为图节点的空间描述符。除空间对象外，我们还通过合理聚合通道来探索通道间的图关系，生成光谱描述符。这些图卷积中的邻接矩阵通过考虑所有描述符间的关联性而获得，从而实现全局感知。通过融合提取的空间与光谱图特征，最终构建出光谱-空间图推理网络（SSGRN）。SSGRN的空间与光谱部分分别称为空间图推理子网络与光谱图推理子网络。在四个公开数据集上的综合实验表明，所提方法相较其他基于图卷积的最优方法具有显著竞争力。