DBSSM: A Dual-Branch Spectral–Spatial Mamba Network for Hyperspectral Image Classification

Abstract

Hyperspectral image (HSI) classification has been widely applied in numerous fields. Although deep learning-based methods have improved classification performance, existing approaches still struggle to balance accuracy and computational efficiency. Convolutional neural network (CNN)-based methods are limited by local receptive fields, whereas Transformer-based methods suffer from high computational complexity, restricting their application in large-scale scenarios. To achieve collaborative optimization of classification accuracy and computational efficiency, a dual-branch spectral–spatial Mamba (DBSSM) network is proposed, which employs a dual-branch architecture with an interactive feature fusion strategy for efficient joint spectral–spatial modeling. The main contributions are as follows: 1) a spatial dual-scan Mamba (SDSM) module is developed to extract rich sequential features via a dual-directional scanning strategy and shared mechanism, enabling long-range dependency modeling with relatively low computational complexity; 2) a spectral group attention (SGA) module is designed to reduce the number of parameters via a grouping strategy while enhancing spectral information interaction using a self-attention mechanism; and 3) an interactive attention fusion module (IAFM) is proposed to achieve bidirectional interactive fusion between spatial and spectral features, thereby further enhancing the feature learning capability. Experimental results on four benchmark hyperspectral datasets demonstrate that DBSSM outperforms state-of-the-art methods in classification performance while maintaining a low parameter count and computational cost, validating its effectiveness. The code is available at https://github.com/Present-Li/DBSSM