Spectral Channel Mixing Transformer with Spectral-Center Attention for Hyperspectral Image Classification

In recent years, the research trend of HSI classification has focused on the innovative integration of deep learning and Transformer architecture to enhance classification performance through multi-scale feature extraction, attention mechanism optimization, and spectral–spatial collaborative modeling. However, due to the excessive computational complexity and the large number of parameters of the Transformer, there is an expansion bottleneck in long sequence tasks, and the collaborative optimization of the algorithm and hardware is required. To better handle this issue, our paper proposes a method which integrates RWKV linear attention with Transformer through a novel TC-Former framework, combining TimeMixFormer and HyperMixFormer architectures. Specifically, TimeMixFormer has optimized the computational complexity through time decay weights and gating design, significantly improving the processing efficiency of long sequences and reducing the computational complexity. HyperMixFormer employs a gated WKV mechanism and dynamic channel weighting, combined with Mish activation and time-shift operations, to optimize computational overhead while achieving efficient cross-channel interaction, significantly enhancing the discriminative representation of spectral features. The pivotal characteristic of the proposed method lies in its innovative integration of linear attention mechanisms, which enhance HSI classification accuracy while achieving lower computational complexity. Evaluation experiments on three public hyperspectral datasets confirm that this framework outperforms the previous state-of-the-art algorithms in classification accuracy.