## 4 Experiments
### Ablation Experiments and Result Analysis
#### 4.4.2 CaSAM  
The CASAM can effectively integrate multi-scale semantic information extracted at different stages of the CNN backbone network, enhance the information interaction and semantic consistency of multi-level scale features, and solve the problem of semantic discontinuity caused by scale jumps. To verify the necessity of this module, the CASAM was removed via ablation experiments for comparative analysis. The data in Row 2 of Table 1 show that removing the CASAM causes the IoU and F1 metrics of the three datasets to exhibit a downward trend. Among them, the F1 value of the WHU-CD dataset decreases by 1.65%, as building changes in this scene mostly have clear structural boundaries and rely on cross-layer feature fusion to improve contour representation accuracy. The visualization results in Column b of Figure 6 further demonstrate that after removing the CASAM, green patches with broken boundaries appear in the building change areas of the WHU-CD dataset, confirming that the lack of cross-layer feature aggregation leads to the loss of  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**EMF**} & \multirow{2}{*}{**CASAM**} & \multirow{2}{*}{**DPEM**} & \multirow{2}{*}{**Transformer**} & \multirow{2}{*}{**CAM**} & \multicolumn{2}{c}{**LEVIR-CD**} & \multicolumn{2}{c}{**WHU-CD**} & \multicolumn{2}{c}{**CLCD**} \\ \cline{6-13}  & & & & & & & **F1** & **IOU** & **F1** & **IOU** & **F1** & **IOU** \\ \hline MDNet & \(\times\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 90.43 & 82.32 & 93.79 & 88.66 & 77.25 & 62.39 \\ MDNet & \(\surd\) & \(\times\) & \(\surd\) & \(\surd\) & \(\surd\) & 90.3 & 83.1 & 93.12 & 89.05 & 77.47 & 62.86 \\ MDNet & \(\times\) & \(\times\) & \(\surd\) & \(\surd\) & \(\surd\) & 88.89 & 80.74 & 91.45 & 85.36 & 71.38 & 58.12 \\ MDNet & \(\surd\) & \(\surd\) & \(\times\) & \(\surd\) & \(\surd\) & 89.39 & 81.5 & 92.56 & 88.24 & 77.42 & 63.35 \\ MDNet & \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & \(\surd\) & 90.69 & 83.05 & 93.5 & 88.58 & 77.65 & 62.61 \\ MDNet & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & \(\surd\) & 89.08 & 81.06 & 91.7 & 85.86 & 72.24 & 58.64 \\ MDNet & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & 90.81 & 83.39 & 94.13 & 89.35 & 78.23 & 64.05 \\ MDNet & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & **91.56** & **84.05** & **94.77** & **90.21** & **78.68** & **64.43** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of ablation experiments on LEVIR-CD, WHU-CD, and CLCD datasets (%).  
Figure 6: The visualization results of ablation experiments on CLCD, WHU-CD, and LEVIR-CD datasets. 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. The images from left to right show the following: A is Image 1, B is Image 2, Label is the ground truth, and then (a)–(h) are the results of removing various modules, including the EMF, CASAM, EMF + CASAM, DPEM, Transformer, DPEM + Transformer, CAM, and the complete MDNet.  
ground object structure information. The above experiments, from the two dimensions of quantitative indicators and qualitative visualization, collectively confirm that the CASAM plays an indispensable role in the change detection network proposed in this paper.