## 5 Discussion
### Ablation Experiment  
To evaluate the significance of the various modules within the proposed method across the entire framework, ablation experiments were conducted on three distinct datasets. Specifically, we assessed the effectiveness of the dual-branch structure, the multi-granularity embedding module, the convolutional cross-substitution attention mechanism, and the residual connections integrated into the overall network design. The efficacy of the dual-branch structure was illustrated by comparing Method 1, which utilizes only the spectral branch, with Method 2, which employs solely the spatial branch. The effectiveness of the multi-granularity embedding module was demonstrated by Method 3, which substitutes this module with a conventional convolutional embedding module. Furthermore, the performance of the convolutional cross-substitution attention mechanism proposed in this study was compared with that of Method 4, which utilizes a traditional linear attention mechanism. Additionally, we analyzed the sharing mechanism in the proposed convolutional cross-replacement attention mechanism against Method 5, which refrains from sharing the projection values \(Q\) and \(V\). Lastly, the necessity of residual connections within the framework was examined by contrasting it with Method 6, which omits residual connections. The comparative results of the ablation experiments across different datasets are illustrated in Figure 12. First, the comparison of the dual-branch method employed in this study with the results from Method 1 and Method 2 validated the effectiveness of the dual-branch structure. The results indicate that relying solely on either spectral or spatial information yields significantly inferior classification performance relative to the integrated dual-branch structure. This finding underscores the importance of combining spectral and spatial information to enhance classification accuracy through the utilization of complementary data. Furthermore, the experimental outcomes associated with Method 3 corroborated the effectiveness of the proposed multi-granularity embedding module. This module has proven to be instrumental in capturing feature information across various scales and levels, thereby enhancing the model's representational capability and ultimately improving classification performance. Additionally, comparisons with Method 4 revealed that the traditional linear attention mechanism did not surpass the performance of our proposed approach. The proposed attention mechanism effectively integrates local and global features while emphasizing essential information, thereby enhancing the model's ability to amalgamate information and improving classification accuracy. The comparison  
Figure 11: The results of the overall accuracy (OA) for various methods with different proportions of training samples in the IP dataset. between Method 5 and our proposed approach demonstrated that, despite the increased computational load associated with Method 5, the results were comparable to those of our method. This outcome indicates that the proposed approach for sharing projected values successfully reduces computational complexity without compromising the model's stability and classification efficiency. Moreover, comparing Method 6 with our proposed approach indicated a decline in classification performance, highlighting the critical importance of residual connections. Residual connections assist in mitigating the vanishing gradient problem, facilitating information flow, and enhancing model stability, which ultimately bolsters both feature representation and classification capability. Furthermore, the differential impact of each module on classification performance across the datasets suggests the influence of factors such as data distribution, noise levels, and feature complexity. Thus, selecting a suitable combination of modules and parameter settings tailored to the specific characteristics of the dataset is essential for achieving optimal performance.