## 4 Results
### CDLD Results  
Table 2 presents the quantitative results of various methods on the CDLD dataset, with the bold part representing that this model has achieved relatively optimal performance. Unlike the HRSCD dataset, FC-Siam-conc, which includes a Siamese encoder and feature concatenation, performs better on the CDLD dataset compared to FC-EF and DTCDSCN, with an F1 score of 61.45%. BiT closely follows, demonstrating the advanced capabilities of the Transformer architecture compared to the traditional UNet models. MFPNet and DSIFN also show excellent performance on the CDLD dataset, with F1 scores exceeding 70%. This outstanding performance is attributed to their multi-scale feature fusion strategies, as the within-class scale variations in the CDLD dataset are much greater than those in the HRSCD dataset.  
Notably, MSCANet achieves the highest performance among the compared methods in terms of recall, F1-score, and IoU, with scores of 67.64%, 71.29%, and 55.39%, respectively, surpassing DSIFN by 3.85%, 0.68%, and 0.81%. However, the proposed method further significantly improves these metrics on the CDLD dataset, especially in terms of recall and IoU, reaching 77.37% and 60.02%, respectively. Additionally, the F1-score of the proposed method is 75.02%, significantly higher than that of all other comparative methods. These results highlight the exceptional capability of the proposed method in detecting farmland change areas, providing a more accurate detection of changes, and reducing both false positives and missed detections, showcasing its superiority and stability in change detection tasks. The Figure 5 shows the experimental results of different methods on various scenarios of the CDLD dataset. Rows 1 to 3 reveal that, when change samples are sparse, other models exhibit poor detection performance, with numerous false positives and missed changes. In contrast, the proposed model maintains good detection capabilities. Rows 4 and 5 demonstrate that, for scenarios involving building encroachment on farmland, both MSCANet and the proposed model exhibit strong detection performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Pre (\%)** & **Rec (\%)** & **F1 (\%)** & **IoU (\%)** \\ \hline FC-EF & 72.75 & 50.3 & 59.48 & 42.33 \\ FC-Siam-conc & 72.23 & 47.53 & 57.34 & 40.19 \\ DTCDSCN & 75.79 & 48.83 & 59.39 & 42.24 \\ BiT & 71.30 & 52.23 & 60.30 & 43.16 \\ MFPNet & 76.42 & 54.98 & 63.95 & 47.01 \\ DSIFN & 77.00 & 54.27 & 63.66 & 46.70 \\ MSCANet & 70.17 & 59.97 & 64.67 & 47.79 \\ The Proposed Method & 62.18 & **69.16** & **65.48** & **48.68** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental results on HRSCD.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Pre (\%)** & **Rec (\%)** & **F1 (\%)** & **IoU (\%)** \\ \hline FC-EF & 71.70 & 47.60 & 57.22 & 40.07 \\ FC-Siam-conc & 73.27 & 52.91 & 61.45 & 44.35 \\ DTCDSCN & 54.49 & 66.23 & 59.79 & 42.64 \\ BiT & 61.42 & 62.75 & 62.08 & 45.01 \\ MFPNet & 83.20 & 60.74 & 70.22 & 54.11 \\ DSIFN & 79.09 & 63.79 & 70.61 & 54.58 \\ MSCANet & 75.36 & 67.64 & 71.29 & 55.39 \\ The Proposed Method & 72.80 & **77.37** & **75.02** & **60.02** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results on CDLD.