## 3 Results
### Comparative Experiments  
Experiments conducted on the DCTransformer model proposed by us have been conducted, and the results have been compared to those of traditional methods, such as DeepHyperX [34]. They have also been compared with the state-of-the-art (**SOTA**) transformer-based techniques, such as the vision transformer (ViT) [14], SSFTT [29], SpectralFormer [17], MorphFormer [13], HybridSN [35], and double-branch multi-attention mechanism network (DBMA) [36]. During the training of DeepHyperX, we set the learning rate to \(1\times 10^{-3}\) and the epoch to 500. A learning rate of \(5\times 10^{-4}\) and epoch of 600 were set for the SpectralFormer's training. We trained the ViT module with a learning rate of \(5\times 10^{-4}\) and an epoch of 1000. For the SSFTT, the learning rate was \(1\times 10^{-3}\), and 100 epochs were used. The results of the classification performances are in an upcoming table, with the best results being emphasized in bold. Table 5 shows the classification performance on the UH dataset. The DCTransformer model we proposed shows an OA of 94.40%, an AA of 94.89%, and kappa coefficients (_kappa_) of 93.92%, which achieves the best performance compared with other methods. Notably, the SSFTT and MorphFormer remain the preeminent methods in the individual categories, yet the model we suggested has a clear superiority in precision overall. The traditional ViT only extracts the global spatial features and ignores the spectral features, which fails to exploit the characteristics of HSIs. Therefore, it has the worst performance. The 3D-CNN-based methods, such as DeepHyperX, can extract spatial-spectral features and therefore achieve a better performance than SpectralFormer, which can effectively extract the spectral information but has a weak capacity to extract spatial features. The SSFTT adopts the Se Block and 2D-CNN network to capture the spatial and spectral features, yet it can only obtain the lowest-frequency information. The training sample size of the Houston dataset is relatively small, and complex models such as DBMA may overfit the training data, resulting in its OA (92.20%) being inferior to the model constituting the simpler HybridSN, with an OA of 92.27%, while our proposed model uses channel attention with the DCT to fully extract spectral features in the spectral branch and a 2D-CNN-based module in the spatial branch to extract and integrate spatial features; then, we combined spatial and spectral features to complete the final classification. The classification accuracy achieved by our DCTransformer model is approximately 5.2% higher than that of  
the SSFTT model. Particularly, the performance for class 15 in the UH HSI DATASET is exceptional, because, compared with other methods, our network uses the DFE, and the DFE can extract the fine spatial features and inter-class information because of gaining a larger receptive field via a gradual extensive dilation rate. Correspondingly, the BFE module has been equipped to extract spectral features with global frequency information and less information loss. Figure 4 shows the visualization of classification maps on the UH dataset and prominently displays some visual qualitative comparisons for class 15. This outcome underscores the effectiveness of the proposed BFE (global spectral feature extraction) module in capturing global spectral features. The classification result of our chosen approach is more refined and has a more distinct edge. Table 6 presents the classification performance metrics for the IP dataset, providing an assessment of its generalization capabilities. The DCTransformer model, as proposed,  
Figure 4: Classification maps for the UH dataset with emphasis on class 15 (Running Track): (**a**) pseudocolor image; (**b**) ground truth; (**c**) DeepHyperX; (**d**) ViT; (**e**) SpectralFormer; (**f**) SSFTT; (**g**) MorphFormer; (**h**) HybridSN; (**i**) DBMA; (**j**) DCTransformer.