## 4 Experiment
### Ablation Study
This limitation arises partly because the Transformer prioritizes overarching information, often at the expense of localized details, and partly due to the absence of multi-scale learning in processing high-level feature maps. The integration of both DCB-ConvNet and FPA modules markedly diminishes instances of overlooked lake boundaries, with the former enhancing the extraction of potent local features and the latter bolstering the comprehension of extensive, complex lake perimeters through multi-scale feature assimilation. However, while the FPA module enriches the model's capacity to discern complex lake borders, it concurrently elevates the rate of false detections even as it curtails instances of overlooked detections. The second row encompasses extensive rivers and complex river networks, positioned in terrains akin to the first group but distinguished by their sinuous boundaries, complicating water bodies extraction processes. It is evident, as shown in the third row, that the baseline framework frequently fails to accurately identify segments of the dense river network, indicating a deficiency in the network's stability concerning river delineation tasks. The incorporation of either a DCB-ConvNet branch or a FPA module significantly mitigates instances of water bodies misidentification. Ultimately, the amalgamation of both the DCB-ConvNet branch and the FPA module results in river delineations that more accurately reflect actual labels, although certain inaccuracies persist. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Metrics (\%)**} \\ \cline{2-5}  & **Precision** & **Recall** & **IoU** & **F1** \\ \hline Swin-Unet-Opt & 91.42 & 90.34 & 83.27 & 90.87 \\ \hline Swin-Unet-Sum & 92.89 & 89.60 & 83.85 & 91.22 \\ \hline SiamSwin-UNet & 92.35 & 90.89 & 84.53 & 91.62 \\ \hline SiamSwin-UNet + DCB-ConvNet & 95.07 & 92.13 & 87.94 & 93.58 \\ \hline SiamSwin-UNet + FPA & 92.36 & 93.20 & 86.53 & 92.78 \\ \hline SiamSwin-UNet + DCB-ConvNet + FPA & 94.82 & 92.79 & 88.31 & 93.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Ablation experiments for the network design. The fourth row illustrates block-distributed aquaculture interspersed among cropland, leading the baseline network to misclassify certain agricultural plots as aquacultural zones. The introduction of the DCB-ConvNet branch and FPA module enhances aquaculture delineation compared to the baseline, but the intricate and fragmented nature of aquaculture distribution results in persistent inaccuracies and omissions in boundary identification against actual labels. The fifth row displays urban water bodies environments, characterized by diverse forms including linear channels, slender streams, and block-like ponds. The baseline network, constrained by its multimodal feature fusion capabilities, exhibits limited efficacy in urban water body delineation. The DCB-ConvNet branch's convolution operations and FPA module's strategy for multi-scale learning on high-level feature maps contribute to enhanced accuracy in urban water bodies extraction. Nonetheless, a degree of misdetection persists, indicating a need for continued exploration in the realm of urban water bodies extraction methodologies.