## 4 Experiments
### Experimental Design
#### 4.2.5 Model Comparison Experiments
\begin{table}
\begin{tabular}{c c c c} \hline
**Experimental Setting** & **Accuracy** & **Recall** & **F1 Score** \\ \hline SwinV2-B & 88.5\% & 86.7\% & 87.9\% \\ Without Multi-Scale Module & 85.3\% & 83.6\% & 84.1\% \\ Without Cross-Modal Alignment Module & 86.3\% & 84.8\% & 85.7\% \\ Without Both Modules & 82.3\% & 81.5\% & 82.9\% \\ Single-Modal Model & 81.6\% & 80.1\% & 81.2\% \\ Ours & 91.5\% & 88.9\% & 86.7\% \\ \hline \end{tabular}
\end{table} 
Table 5: Ablation study on the various components of our model. In this study, we evaluated the performance of the classification model on different datasets by confusion matrix. Table 6 illustrates the classification results for the full validation dataset, the 2016-2017 subset, the 2018-2019 subset, and the 2020-2021 subset. In the full validation dataset, the model performs well on the forest category, with an accuracy of 0.960, and the accuracy of the water category is also quite high, with an accuracy of 0.968. However, the house category has 22.6% of instances misclassified as forest, which may be due to the similarity between the two categories in some features. The results for the 2016-2017 subset show that the forest category has the highest accuracy of 0.960. The house class has 19.7% misclassified as forest, which is consistent with the results on the full validation dataset, indicating that the model has some challenges in distinguishing between the two classes. The classification performance of the 2018-2019 subset was similar to that of the 2016-2017 subset, with an accuracy of 0.969 for the forest class, while 19.9% of the house class was misclassified as forest. This further confirms the difficulty of the model in distinguishing between the house and forest categories. The results for the 2020-2021 subset show that the accuracy of the forest category remains at 0.960, which is the same as the 2016-2017 subset. The house class has 19.6% misclassified as forest, indicating that the model still faces challenges in distinguishing between the two classes even in the most recent subset of the data. These metrics give us an overview of the performance of the model on the different categories, as well as the overall classification performance. By comparing the confusion matrices of different subsets, we were able to analyze the performance of the model in different time periods and identify possible classification difficulties of the model on some categories. Figure 7: Examples of the original images (**a**,**b**) and results of their inference results compared with the ground truth (**c**,**d**). White represents the match between ground truth and inference, green represents the ground truth that is not covered by the inference results, and red represents the inference results that do not cover ground truth. We present a comparison of different fusion methods in Table 7. The cross-attention mechanism proves to be the most effective for multimodal feature fusion [21], yielding an accuracy of 81.5%, a macro F1 score of 78.4%, and a cross-entropy loss of 0.22. In contrast, simple concatenation results in a lower accuracy of 78.2% and a higher cross-entropy loss of 0.26. This difference highlights the limitations of static fusion methods, which treat all features equally, leading to suboptimal integration of multimodal data. By contrast, the cross-attention mechanism dynamically adjusts the weights of features from different modalities, allowing the model to emphasize the most relevant features from each modality. This adaptive feature weighting significantly improves model robustness, enabling better generalization to unseen data and yielding more accurate predictions. The results also indicate that sum and average fusion methods, which rely on simplistic mathematical operations, are the least effective, with