A Multi‐Scale Spatial‐Spectral Mamba Network for Hyper‐Spectral Image Classification

ABSTRACT

Convolutional Neural Networks (CNNs) and Transformers have achieved remarkable success in hyperspectral image (HSI) classification. However, CNNs are limited in modelling long‐range spatial dependencies, while the quadratic computational complexity of Transformer restricts their application to high‐dimensional HSI data. Recently, state‐space models (SSMs), particularly Mamba, have shown great potential due to their linear computational complexity and powerful sequence modelling capabilities. However, the original Mamba model is designed for one‐dimensional sequence modelling and struggles to effectively handle the inherent three‐dimensional spatial‐spectral characteristics of HSI. Furthermore, it lacks dedicated considerations for the deep fusion of spectral and spatial information and the integration of global context and local details. To address this issue, we propose a novel Multi‐Scale Spatial‐Spectral Mamba Network (MSS‐MambaNet) for efficient and accurate HSI classification. Specifically, MSS‐MambaNet consists of four key components: First, the Multi‐scale Local Spectral Feature Extraction Module (MSLSFE) captures spatial‐spectral features at different scales through a parallel multi‐branch architecture. Second, the Spatial Mamba Encoder employs an innovative dimensionality reorganization strategy to model global spatial context while preserving spatial structure. Third, the Spectral Mamba Encoder incorporates a bidirectional split‐scan mechanism to comprehensively capture the complex dependencies between spectral bands. Finally, the Global Spatial‐Spectral Feature Fusion Module (GSSFF) deeply integrates the complementary information of spatial and spectral features through multi‐scale dilated convolutions and a detail‐enhanced attention mechanism. MSS‐MambaNet achieves overall accuracies of 97.19%, 94.25%, and 97.38% on the Pavia University, Indian Pines, and Salinas datasets, respectively, outperforming state‐of‐the‐art methods in both classification accuracy and computational efficiency. The code will be available at https://github.com/ZhangSaida/Mss‐MambaNet .