## 4 Experiments
### Experimental Design
#### 4.2.5 Model Comparison Experiments
In terms of the number of parameters, our model requires 70 M, which is higher than other methods but leads to comprehensive performance advantages. CSwin-B and Swin \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Recall** & **F1 Score** & **Throughput** & **FLOPs** & **Parameters** \\ \hline SwinV2-B & 88.5\% & 85.8\% & 86.7\% & 67/s & 45.2 & 50 M \\ SwinUnet [18] & 89.2\% & 86.3\% & 87.2\% & 56/s & 48.5 & 65 M \\ DS-TransUNet-B [19] & 87.3\% & 85.9\% & 86.8\% & 61/s & 43.8 & 55 M \\ CSwin-B [20] & 89.3\% & 87.8\% & 88.8\% & 60/s & 46.7 & 60 M \\ Ours & 91.5\% & 88.1\% & 88.9\% & 74/s & 52.3 & 70 M \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of model performance across different state-of-the-art methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Embedding** & **Node Count** & **Graph Density** & **Accuracy** & **Graph Distance** \\ \hline GraphSAGE & 2000 & 0.35 & 85.2\% & 0.15 \\ Node2 Vec & 2000 & 0.35 & 84.5\% & 0.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of graph embedding methods. Unet require 60 M and 65 M parameters, respectively, while SwinV2-B and DS-TransUNet-B have relatively lower parameter counts of 50 M and 55 M. Despite the increased parameter size, our model demonstrates that a moderate increase in complexity can yield substantial performance gains, achieving a superior trade-off between model size and capability. Overall, the experimental results demonstrate that our proposed method excels in accuracy, recall, F1 score, and throughput, outperforming all competing models. Although it incurs slightly higher computational and parameter costs, these are well compensated by the remarkable improvements in performance. These results highlight the practical value and potential of our method in addressing challenging tasks with high efficiency and reliability. The results of the ablation study in Table 5 indicate that the baseline SwinV2-B model, without the addition of the multi-scale and cross-modal alignment modules, achieved an accuracy of 82.3%, a recall of 81.5%, and an F1 score of 82.9%. Removing the multi-scale attention module led to a decrease in accuracy to 85.3%, demonstrating the module's significant role in enhancing the model's ability to process dual-modal data through improved feature extraction. When the cross-modal alignment module was excluded, the accuracy slightly decreased to 86.3%, suggesting that this module aids in the fusion of dual-modal information, enhancing the model's understanding of inter-modal relationships. In the joint ablation of both modules, the model's performance further declined, highlighting the complementary contributions of the two modules in improving dual-modal data processing. In contrast, the single-modal model configuration yielded an accuracy of 81.6%, emphasizing the advantage of a dual-modal approach. The enhanced dual-modal model, incorporating both the multi-scale attention mechanism and cross-modal alignment module, achieved superior performance with an accuracy of 91.5%, a recall of 88.9%, and an F1 score of 86.7%, demonstrating that the proposed improvements significantly bolster the model's capability to handle dual-modal information effectively. The model achieves high segmentation accuracy across all subsets, as shown by the focal loss and pixel accuracy values in Table 6. Figure 7 provides examples of segmentation results compared with ground truth, demonstrating the performance of the model after fine-tuning. Figure 8 illustrates the classification results for the full 426 validation dataset, the 2016-2017 subset, the 2018-2019 subset, and the 2020-2021 subset. These findings confirm the effectiveness of the multi-scale and cross-modal alignment modules in improving the SwinV2-B model's performance on dual-modal tasks, underscoring the contributions of both feature extraction and modal alignment to the overall performance enhancements.