Transformers experience quadratic computational complexity in hyperspectral image (HSI) classification tasks, which can result in error propagation and memory usage issues. Recently, Mamba architectures built upon the State Space Models have supplanted Transformers across various domains to accomplish long-range sequence modeling capability while demonstrating the advantages of linear computational efficiency. However, employing the basic Mamba model for HSI classification has problems associated with the extraction of spatial and spectral features. Motivated by this, we propose the DS-Mamba, a novel depthwise separable Mamba for HSI classification. Specifically, to extract the spatial and spectral features more efficiently, we design a depth spatial Mamba block (DSpaM), a depth spectral Mamba block (DSpeM) and a feature enhancement module. These blocks use depthwise separable convolution in conjunction with the basic Mamba block to improve classification accuracy while maintaining a low computational cost. Subsequently, to enhance the classification performance, feature weights are adjusted and spatial as well as spectral information are integrated through the feature fusion module. Finally, the feature information is enhanced and categorized by a classification module with Efficient Channel Attention (ECA). Through comparative experiments, DS-Mamba achieved overall accuracies of 96.54%, 91.52%, and 94.89% on the Pavia University, Hanchuan, and Houston datasets, respectively. Its classification performance surpassed that of several advanced transformer-based methods. Furthermore, DS-Mamba has lower model parameters and floating point operations (FLOPs), with only 137.74K parameters and 12.52G FLOPs recorded on the Pavia University dataset.
Wei et al. (2026) studied this question.