SEDGM: A Structure-Enhanced Spatial–Spectral Dynamic Gating Mamba for Hyperspectral Image Classification

With the rapid development of hyperspectral image classification (HSIC) technology, its applications in geological exploration and environmental monitoring have become increasingly prominent. Recently, Mamba has garnered significant attention owing to its outstanding performance in long-range sequence modeling and linear computational complexity. However, Mamba still exhibits significant limitations in HSIC: first, it does not fully consider the hierarchical spatial–contextual representation and nonlinear spectral interactions in hyperspectral images; second, its sequential processing approach leads to the loss of spatial structural information and feature redundancy. In response, this study proposes a structure-enhanced spatial–spectral dynamic gating Mamba (SEDGM) that leverages the collaborative design of spatial and spectral gating Mamba mechanisms to extract and exploit key regional features of hyperspectral data. The spatial branch employs hierarchical gating Mamba (HGM) to capture multidirectional pixel sequences and extract the hierarchical spatial features and their intrinsic relationships. In contrast, the spectral branch utilizes a random shuffled gating Mamba to disrupt the fixed order of traditional spectral sequences and capture higher order spectral couplings, effectively characterizing the cooperative variation patterns of spectral features. Both branches employ a dynamic gating mechanism that weights features based on sequence centrality, dynamically activating feature sequences. Additionally, shape-specific offset-aware attention (OAA) is incorporated into each branch to enhance the structured features that were lacking in the Mamba sequences. Finally, a spectral-oriented feature review module (SOFRM) is incorporated to achieve dynamic feature fusion and optimized refinement. Experiments were conducted on four large-scale benchmark hyperspectral imaging (HSI) datasets, with SEDGM achieving significant improvements in classification performance, validating the effectiveness of this approach in HSIC tasks. The code is available at https://github.com/shuai2023-hash/SEDGM