## 4 Results
### Ablation Experiment  
In this subsection, we conduct a series of experiments to verify the influence of the MFCF module and decoder module on the overall network performance.  
To evaluate the efficacy of the MFCF module and decoder module proposed in this study, an ablation experiment was conducted on the Levi-CD dataset. The purpose was to analyze the impact of these modules within the network by removing and combining them. The Base setting represents a feature extraction network utilizing 3D ResNet50 as its foundation without any additional modules. The Base + MFCF setting indicates that MFCF modules are added to the Base network. The Base + De setting represents a network that adds decoder modules on top of Base. Our represents the final network structure proposed in this article. The ablation experimental results are shown in Table 2. To make the table more readable, the best data are marked in bold and the data are displayed as a percentage.  
Upon integrating the MFCF module into the Base network, there was a notable increase of 0.22% in the \(F1\) score and approximately 0.15% in the \(IOU\) indicator. Furthermore, incorporating the De module led to a significant improvement of 0.95% in the \(F1\) score and 1.5% in the \(IOU\) indicator compared to the Base network. Ultimately, the final network architecture in this study demonstrated enhancements of approximately 1.06% in the \(F1\) score and 1.56% in the \(IOU\) indicator when compared to the Base network.  
The ablation experiment further demonstrates the effectiveness of our model. Our method can better distinguish positive samples (i.e., changed areas) and decrease the occurrence of incorrect tests. Additionally, the results of the ablation experiment indicate that integrating the MFCF module and De module into this network can improve the \(F1\) score and \(IOU\) indicator. Specifically, the De module plays a crucial role in enhancing the precision of change detection.  
Figure 12 presents visualizations of four different methods applied to the LEV-IR-CD dataset. The figure displays four representative samples from the dataset, using various colors to distinguish error-detected change areas (FP) shown in red and no change areas (FN) shown in green, providing improved visual clarity.  
The visualized outcomes of the ablation experiment are depicted in the figure above. When compared to the results from the basic network structure, it is clear that the MFCF module and the De module, as introduced in this paper, show reduced false boundary detection in transitional areas and improved detection efficacy in rapidly changing regions. Among the four ablation experiments compared, the final method presented in this article displays the lowest missed detection rate and false detection rate in the detection area, as well as the highest detection accuracy. Overall, the MFCF module and De module proposed in this study prove to be effective in enhancing performance in change detection. Particularly, the De module plays a significant role in improving the overall detection accuracy of the network.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Network Setting**} & \multicolumn{2}{c}{**LEVIR-CD**} \\ \cline{2-5}  & **MFCF** & **De** & **F1** & **IOU** \\ \hline Base & \(\times\) & \(\times\) & 89.85 & 81.79 \\ Base + MFCF & \(\surd\) & \(\times\) & 90.07 & 81.94 \\ Base + De & \(\times\) & \(\surd\) & 90.80 & 83.29 \\ Our & \(\surd\) & \(\surd\) & **90.91** & **83.35** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The ablation results of four methods on the LEVIR-CD dataset. The highest score is marked in bold. All the scores are described in percentages (%).