Hyperspectral Image Classification Based on Subgraph-Dependent Neural Network

Classification methods based on subgraph neural networks (SNNs) are rarely explored, and its advantage is that it can alleviate the neighbor explosion problem. After applying SNNs to hyperspectral image (HSI) classification, the imbalanced topology structure in the internal subgraph leads to poor classification performance due to the intraclass and interclass spectral feature variation. Based on this, we proposed a novel subgraph-dependent neural network (SGDNet) for HSI classification. Specifically, we firstly segmented the large graph to a series of subgraphs, and proposed a subgraph-dependent convolution method for imbalanced subgraph structure to achieve effective feature smoothing within each subgraph. It mainly utilized the degree feature embedding with the residual to improve the feature diversity as well as the intraclass distance of clustering measurement to determine the optimal subgraph convolution layers in a feedback way. Secondly, we developed the strategy of the long-range dependency to address the inevitably local structure dependence among nodes within subgraphs. It utilized an anchor point based position coding method to capture the relative positions of unlabeled nodes in relation to all labeled nodes within the graph and further constructed the structural loss function for effective training, achieving subgraph optimization. Comprehensive experiments demonstrated the superior performance of the SGDNet model on three publicly available HSI datasets, compared to other popular methods. The source code will be available at https://github.com/lichao226211/SGDNet.