## 5 Discussion  
The experimental results demonstrate that the proposed model significantly outperforms existing methods, including MFPNet and MSCANet, on both the CLCD and HRSCD datasets, fully validating its effectiveness in capturing true farmland changes. Specifically, on the HRSCD dataset, the proposed method achieves an F1-score of 65.48% and an IoU of 48.68%, with both these values exceeding those of the best-performing baseline method MSCANet. On the CLCD dataset, the proposed method further achieves an F1-score of 75.02% and an IoU of 60.02%, highlighting its superior performance. This advantage can be attributed to the effective integration of multi-scale feature extraction and attention mechanisms, which significantly enhance the model's capacity to characterize complex change regions. In addition, the results of an ablation study further verify the critical contributions of the Multi-Scale Mixer, the Cross-Channel Spatial Encoder, and the Multi-Scale Attention Decoder to the overall performance. Specifically, the introduction of the Multi-Scale Mixer alone leads to a substantial increase in recall, reaching 79.08% and 64.56% on the two datasets, with notable improvements in both F1-score and IoU. These results indicate that the Multi-Scale Mixer plays a vital role in enhancing the representation of multi-scale features and capturing potential change regions. However, this enhancement comes at the cost of reduced precision, which drops to 68.35% and 63.89%, suggesting that, while the Multi-Scale Mixer effectively reduces missed detections, it still struggles to fully suppress false alarms. To address this issue, the Cross-Channel Spatial Encoder is further introduced, resulting in moderate improvements in precision across both datasets. This finding demonstrates that the Cross-Channel Spatial Encoder effectively enhances the spatial dependency and cross-channel interactions within the features, thereby improving the model's sensitivity and accuracy in detecting change regions and enabling more comprehensive change capture. Building on this foundation, the addition of the Multi-Scale Attention Decoder achieves a balanced improvement in both precision and recall on the CLCD dataset while ensuring consistent leadership in terms of the F1-score and IoU. Similarly, on the HRSCD dataset, the proposed model maintains superiority in terms of recall, the F1-score, and IoU. This confirms that the Multi-Scale Attention Decoder not only effectively integrates multi-scale contextual information but also enhances the preservation and characterization of fine-grained changes through the attention mechanism. This highlights the critical role of the decoder in multi-scale information aggregation and fine-grained change detection. However, in the complex background of the HRSCD dataset, the intricate foreground-background relationships may lead to minor confusion or redundancy in certain feature representations. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**HRSCD**} & \multicolumn{4}{c}{**CLCD**} \\ \cline{2-9}
**Method** & **Pre (\%)** & **Rec (\%)** & **F1 (\%)** & **IoU (\%)** & **Pre (\%)** & **Rec (\%)** & **F1 (\%)** & **IoU (\%)** \\ \hline \(L(P_{\text{s}_{4}})\) & 62.65 & 64.84 & 63.74 & 46.78 & 71.73 & 76.73 & 74.14 & 58.91 \\ \(L(P_{\text{s}_{4}})+L(P_{\text{s}_{4}})+L(P_{\text{s}_{4}})+L(P_{\text{s}_{4 }})\) & 62.18 & 69.16 & 65.48 & 48.68 & 72.80 & 77.37 & 75.02 & 60.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation of multi-scale loss on CLCD and HRSCD datasets. Despite the significant improvements achieved across various evaluation metrics, there is still room for further optimization. The current model architecture could be further enhanced to better accommodate the diverse patterns of farmland changes, particularly when dealing with highly heterogeneous change types.