Recently, graph convolutional networks (GCNs) have been developed to explore
spatial relationship between pixels, achieving better classification
performance of hyperspectral images (HSIs). However, these methods fail to
sufficiently leverage the relationship between spectral bands in HSI data. As
such, we propose an adaptive cross-attention-driven spatial-spectral graph
convolutional network (ACSS-GCN), which is composed of a spatial GCN (Sa-GCN)
subnetwork, a spectral GCN (Se-GCN) subnetwork, and a graph cross-attention
fusion module (GCAFM). Specifically, Sa-GCN and Se-GCN are proposed to extract
the spatial and spectral features by modeling correlations between spatial
pixels and between spectral bands, respectively. Then, by integrating attention
mechanism into information aggregation of graph, the GCAFM, including three
parts, i.e., spatial graph attention block, spectral graph attention block, and
fusion block, is designed to fuse the spatial and spectral features and
suppress noise interference in Sa-GCN and Se-GCN. Moreover, the idea of the
adaptive graph is introduced to explore an optimal graph through back
propagation during the training process. Experiments on two HSI data sets show
that the proposed method achieves better performance than other classification
methods.

提出了一种自适应的空间 - 光谱图卷积网络，其中包括一个空间图卷积子网络、一个光谱图卷积子网络和一个图交叉注意力融合模块，通过加入注意力机制，融合了空间和光谱特征，并探索了优化图形的思想，在两个高光谱图像数据集上实验证明了该方法比其他分类方法具有更好的性能。