## 4 Results
### Ablation Experiments and Loss Curves  
In this section, ablation experiments were conducted to assess the contribution of each component to the proposed method. The experiments were performed on the CDLD dataset, and the quantitative results are presented in Table 3. The baseline model, referred to as \"Base\", is MSCANet, with the following performance metrics: a precision (Pre) of 75.36%, a recall (Rec) of 67.64%, an F1-score of 71.29%, and an Intersection over Union (IoU) of 55.39%. First, the model with the added Multi-Scale Mixer, labeled as \"Multi-Scale Mixer\", showed a decrease in precision to 68.35% but a significant increase in recall to 79.08%. This resulted in an improvement in the F1-score to 73.32% and an IoU of 57.88%. These results suggest that, while the Multi-Scale Mixer reduces false positives, it may limit the model's ability to capture a wider range of change areas. Next, incorporating the Cross-Channel Spatial Encoder into the model, referred to as \"Cross-Channel Spatial Encoder\", showed further improvement, with precision increasing to 70.24%, recall to 78.35%, F1-score to 74.07%, and IoU to 58.82%. These enhancements indicate that the Cross-Channel Spatial Encoder effectively improves the model's sensitivity and accuracy in detecting change areas, thus enabling it to capture them more effectively. Finally, the model incorporating the Multi-Scale Attention Decoder, referred to as \"Multi-Scale Attention Decoder\", achieved the best performance across all metrics: a precision of 72.80%, a recall of 77.37%, an F1-score of 75.02%, and an IoU of 60.02%. The addition of the Multi-Scale Attention Decoder significantly enhanced the model's accuracy and detail retention in  
Figure 5: Visualization of experimental results on CDLD dataset: (**a**) Image1; (**b**) Image2; (**c**) Label; (**d**) FC-EF; (**e**) FC-Siam-conc; (**f**) DTCDSCN; (**g**) BiT; (**h**) MFPNet; (**i**) DSIFN; (**j**) MSCANet; (**k**) the proposed method. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**CLCD**} & \multicolumn{4}{c}{**HRSCD**} \\ \cline{2-9}  & **Pre (\%)** & **Rec (\%)** & **F1 (\%)** & **IoU (\%)** & **Pre (\%)** & **Rec (\%)** & **F1 (\%)** & **IoU (\%)** \\ \hline Base & 75.36 & 67.64 & 71.29 & 55.39 & 70.17 & 59.97 & 64.67 & 47.79 \\ +MSM & 68.35 & 79.08 & 73.32 & 57.88 & 63.89 & 64.56 & 64.22 & 47.81 \\ +CCSE & 70.24 & 78.35 & 74.07 & 58.82 & 63.92 & 65.59 & 64.32 & 47.89 \\ OURS & 72.80 & 77.37 & 75.02 & 60.02 & 62.18 & 69.16 & 65.48 & 48.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison on CLCD and HRSCD datasets. detecting change areas, demonstrating the decoder's critical role in integrating multi-scale information and implementing effective attention mechanisms. These ablation experiments validate the effectiveness of each component and confirm the advantages of the proposed method in the farmland change detection task. In the loss function experiments, the model demonstrated a consistent decreasing trend in the loss values across both the training and validation sets, as shown in Figure 6, indicating that the model effectively reduced prediction errors during the learning and optimization process. On the CLCD dataset, the loss function stabilized around the 40 th epoch, suggesting that the model successfully captured key features related to farmland change and reached a stable state, enabling accurate change detection. This indicates that the model's performance on this dataset approached an ideal state, exhibiting a strong learning ability and adaptability. On the HRSCD dataset, the loss on the training set stabilized around the 80 th epoch. Although the validation set showed an overall downward trend, there were some fluctuations. These fluctuations may be attributed to the complexity of the dataset and the diversity of feature levels.