LKMA: Learnable Kernel and Mamba With Spatial–Spectral Attention Fusion for Hyperspectral Image Classification

Transformer models have achieved remarkable success in hyperspectral image classification (HSIC) owing to their strong global modeling capability. However, their quadratic complexity significantly limits their computational efficiency. Recently, Mamba has been applied to HSIC because of its linear complexity, yet it still suffers from an imbalance between global and local modeling. To overcome these challenges, this paper proposes a novel Learnable Kernel and Mamba with Spatial-Spectral Attention Fusion (LKMA) framework, which enables the extraction of global-local spatial-spectral features (SSF) while enhancing edge feature representation. For local feature extraction, the proposed Multi-Scale Spatial-Spectral Feature Generation (MSSFG) module captures local SSF by employing multi-scale learnable dilation convolutions for spatial features and multi-scale dilation convolutions for spectral features. For global feature extraction, a Global Hidden Mixing Mamba (GHMM) module is introduced, which projects hyperspectral image (HSI) features from the feature space to the hidden state space via a hidden state mixing mechanism. This enables the model to capture contextual semantic information and local details from the HSI. To further explore the synergistic effect between spatial and spectral information, the Spatial-Spectral Attention Fusion (SSAF) module integrates semantic information across multiple feature groups by combining Semantic Grouped Spatial Attention (SGSA) and Progressive Spectral Self-Attention (PSSA), enhancing spatial-spectral representations. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art approaches for HSIC.