## 4 Experiments
### Comparative Experiment and Result Analysis
#### 4.5.2 Comparisons on the WHU-CD Dataset  
Table 3 presents the performance comparison of each method on the WHU-CD dataset. MDNet demonstrates comprehensive advantages in complex urban building change scenarios, with a recall of 93.84%, F1-score of 94.77%, and IoU score of 90.21%, significantly outperforming existing methods. Compared with the suboptimal method WS-Net++, MDNet improves the F1-score and IoU by 0.75% and 0.8%, respectively, with core advantages in precision optimization under high recall and adaptability to complex scenarios. For the large-scale urban expansion and high-density building change scenarios in the WHU-CD dataset, MDNet enhances differential features through the DPEM, increasing the IoU by 4.57% compared with AMTNet, verifying its precise positioning capability for large-scale change area boundaries. It is worth noting that although WS-Net++ leads with the highest precision of 95.36%, its recall is relatively low. It is speculated that it may overfit the significant change regions in the training data, resulting in insufficient fine change detection capability.  
The qualitative visualization results are shown in Figure 8, and the differences among different methods in typical scenarios are significant: In the first-row samples, FC-EF and BiT exhibit obvious false positives (FPs, red area diffusion) at the edges of large newly  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **P** & **R** & **F1** & **IOU** \\ \hline FC-EF & 80.87 & 75.43 & 78.05 & 64.01 \\ SNUNet & 83.25 & 91.35 & 87.11 & 77.17 \\ BiT & 83.05 & 88.8 & 85.83 & 75.18 \\ AMTNet & 92.86 & 91.99 & 92.27 & 85.64 \\ WS-Net++ & **95.36** & **92.7** & **94.02** & **89.41** \\ MDNet (Ours) & **94.35** & **93.84** & **94.77** & **90.21** \\ \hline \hline \end{tabular} The red bold font indicates the best performance and the black bold font indicates the second best.  
\end{table}
Table 3: Performance evaluation of the WHU-CD dataset (%).  
Figure 7: Visualization results of different methods on the LEVIR-CD dataset. In the prediction results, white represents true positive (TP), black represents true negative (TN), red represents false positive (FP), and green represents false negative (FN). In brief, the lower the proportion of red and green, the better the prediction performance of the model. A and B represent two-phase remote sensing images, Label is the ground-truth change annotation, FC-EF, SNUNet, BIT, AMTNet, WS-Net++ are comparative models, and MDNet (Ours) is the proposed model.  
built regions, while MDNet achieves edge sharpening and spatial integrity preservation of change regions through the CASAM's cross-layer feature aggregation and Transformer's long-range dependency modeling; in the second- and third-row samples, methods such as SNUNet and AMTNet easily misclassify unchanged regions as changes (FP) in similar building clusters. MDNet, however, explicitly enhances spectral-structural difference features through the DPEM, effectively distinguishing buildings from impervious surfaces with similar materials and colors, demonstrating high sensitivity to semantic differences in complex ground objects.