Community Structure Guided Network for Hyperspectral Image Classification

Recently, the hypergraph convolutional network (HGCN) has attracted increasing attention in hyperspectral image (HSI) classification. Compared to graph convolutional networks, HGCN has a stronger ability to mine nonlinear high-order correlations. However, the problems of intraclass variability and interclass similarity exist due to the effects of light, environment, and sensor bias, resulting in insufficient reliability of hypergraphs constructed by directly utilizing the original spectral features. Motivated by the observation that the land cover in HSI contains the spatial distribution semantic information of community structures, which can be used to extract deeper contextual semantic features, we propose a novel community structure guided network (CSGNet) for HSI classification. Specifically, CSGNet adopts a dual-branch architecture: the HGCN branch focuses on superpixel-level high-order feature extraction, while the convolutional neural network (CNN) branch enhances pixel-level local features. In HGCN branch, a novel reliable hypergraph construction approach is introduced, which strikes a balance between depth-first search (DFS) and breadth-first search (BFS), effectively representing different community structure features and improving the ability of edge detection. Meanwhile, kernel function mapping is used to achieve more accurate node connections and enhances classification within classes. Finally, to achieve balanced training of the HGCN and CNN branches, we add their cross-entropy loss as an auxiliary component in the backpropagation process. Experimental results demonstrate that CSGNet outperforms the state-of-the-art methods. The code will be released at https://github.com/KustTeamWQW/CSGNet.