DSS-Mamba: Deformable Spatial–Spectral State-Space Model for Hyperspectral Land Cover Classification

State space models (SSMs), particularly Mamba, have garnered significant attention from researchers owing to their efficient balance between computational efficiency and model performance, and they also provide a performance breakthrough for accurate land cover classification of hyperspectral images (HSIs). However, HSI data feature high spectral dimensionality and dense spatial information, so efficiently constructing Mamba sequences that align with the structural characteristics of HSI data remains a critical unresolved issue. To address this challenge, we propose an efficient deformable spatial-spectral Mamba network (DSS-Mamba), which aims to efficiently mine valuable spatial-spectral features from HSIs. To achieve this goal, we design a two-branch architecture consisting of a spatial deformable Mamba and a spectral bidirectional Mamba: the spatial deformable scanning Mamba (SDSM) integrates a spatial deformable state space model (SD-SSM) and adaptively focuses on salient feature regions through a dynamic scanning strategy, while the spectral bidirectional scanning Mamba (SBSM) incorporates a spectral bidirectional state space model (SB-SSM) and fully exploits spectral dimensional information via a bidirectional scanning mechanism. During feature processing, we propose a spatial-spectral complementary fusion (SSCF) module, which refines feature weights by means of a dual-threshold enhancement unit (DTEU) to realize efficient processing and dynamic fusion of spatial-spectral features. Extensive experiments demonstrate that DSS-Mamba effectively balances the fine-grained capture of spatial-spectral features and computational efficiency through the adaptive capture of spatial features by the deformable structure, the in-depth mining of spectral information via bidirectional scanning, and the complementary fusion of the spatial-spectral module. Consequently, it achieves superior classification accuracy in land cover classification.