Hyperspectral Image Classification With Mamba

Local and global spectral and spatial information is crucial for hyperspectral image (HSI) classification. However, modeling the global context has been challenging due to the limitations of receptive fields and quadratic complexity. Mamba’s ability to leverage long-range dependencies with linear computational complexity offers an effective approach to alleviate this issue; however, it does lead to the loss of local detail information. To address this challenge, we propose a novel local-to-global Mamba for HSI classification, termed MambaLG. MambaLG consists of a dual-branch strategy, comprising two core modules: a local and global spatial modeling module (SpaM) and a short- and long-range spectral dynamic perception module (SpeM). In the SpaM, the local and global spatial information is sequentially extracted and integrated, aiming to capture global spatial semantics while preserving the integrity of local 2-D spatial structures. In the SpeM, we utilize local spectral extraction, spectral grouping, and spectral dynamic correlation clustering (SDCC) modules, leveraging Mamba’s strengths in exploring long-range dependencies for more precise short- and long-range spectral feature modeling. Additionally, we introduce a gate attention unit into MambaLG and design a more efficient and interpretable manner for merging spatial and spectral features. Experimental results across multiple datasets (encompassing urban and agricultural scenes) indicate that MambaLG surpasses state-of-the-art algorithms regarding classification accuracy (CA) and inference speed. Comprehensive ablation studies substantiate the advantages of MambaLG in modeling local and global spatial context, enhancing short- and long-range spectral perception, and fusing spatial and spectral information. The codes will be openly available at https://github.com/danfenghong/IEEE_TGRS_MambaLG to facilitate the reproduction of experimental results.