TGMN: Two-Stage Graph Convolutional Mamba Network for Hyperspectral Image Classification

Local spectral features and global spatial context are essential for hyperspectral image (HSI) classification. However, existing methods based on convolutional neural networks (CNNs), graph convolutional networks (GCNs), and Transformers often rely on multibranch structures to separately extract and fuse local and global features, resulting in high computational complexity and redundant information that can negatively affect classification performance. To address these issues, we propose a two-stage graph convolutional mamba network (TGMN) that enables efficient modeling of local and global features through sequential intrasubgraph local feature extraction and intersubgraph global information learning. Specifically, in the first stage, we partition the HSI into superpixel regions and treat each superpixel as a subgraph, where a GCN is applied to aggregate spectral-spatial features within each subgraph. We further design a downsampled subgraph feature reconstruction (DSFR) module that dynamically selects key nodes to reduce redundancy, highlight critical features, and enhance model representation capability. In the second stage, the Mamba network models the global dependencies between subgraphs and introduces a region-relation aware absolute positional encoding (RAPE) module. This module encodes spatial positional information into embedded vectors by integrating the relative distance and direction between the geometric center of each superpixel and the image center, which are then deeply fused with the feature matrix to improve spatial relationship comprehension. The two-stage sequential structure ensures effective local and global feature extraction, avoiding the high computational complexity and redundancy issues commonly associated with multibranch models. Experiments on three benchmark datasets demonstrate its superiority, achieving classification accuracies of 98.54%, 98.30%, and 96.94% on the Indian Pines, Dioni, and Honghu datasets, respectively. Compared to state-of-the-art methods, TGMN achieves higher classification accuracy with significantly lower computational cost, demonstrating its efficiency and effectiveness for HSI classification.