## 3 Results
### Analysis of Experimental Results  
According to the experimental results for the extraction of water from the SAR images shown in Figures 5-8, we can observe significant differences in image details, as well as a high degree of diversity in water types and their features. Tables 1-4 provide a detailed list of the water detection performance indicators for the four networks in three experiments. Specifically, in terms of detecting small water bodies, GCAFF-Net significantly outperforms U-Net, particularly in Scene I, where U-Net missed a large number of small water bodies, whereas GCAFF-Net exhibited a higher detection accuracy. Furthermore, in Scene I, the PA, IoU and F1-Score of GCAFF-Net are significantly higher than other networks, which demonstrates that GCAFF-Net can more precisely extract small pond water bodies and the edge information of water bodies, indicating its higher accuracy and stronger robustness. Further referring to the data in Table 3, we can find that both U-Net and GCAFF-Net \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Networks**} & \multicolumn{4}{c}{**Water**} & \multicolumn{4}{c}{**Background**} \\ \cline{2-9}  & **PA (\%)** & **IoU (\%)** & **Recall (\%)** & **F1-Score (\%)** & **PA (\%)** & **IoU (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline U-Net & 87.56 & 76.68 & 84.24 & 86.80 & 95.96 & 93.60 & 97.24 & 96.70 \\ MF2 AM & 82.83 & 73.16 & 86.25 & 84.50 & 96.82 & 92.97 & 95.90 & 96.36 \\ Deeplabv3+ & 82.59 & 76.57 & 91.31 & 86.73 & 98.11 & 94.16 & 95.90 & 96.99 \\ Ours & 89.59 & 82.10 & 92.93 & 90.17 & 98.40 & 95.53 & 97.34 & 97.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of water detection results among various networks in Scene III. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Networks**} & \multicolumn{4}{c}{**Water**} & \multicolumn{4}{c}{**Background**} \\ \cline{2-9}  & **PA (\%)** & **IoU (\%)** & **Recall (\%)** & **F1-Score (\%)** & **PA (\%)** & **IoU (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline U-Net & 92.65 & 80.72 & 83.12 & 89.34 & 94.89 & 94.05 & 99.06 & 96.93 \\ MF2 AM & 89.23 & 85.27 & 95.06 & 92.05 & 98.79 & 96.10 & 97.24 & 98.01 \\ Deeplabv3+ & 88.28 & 81.12 & 90.91 & 89.58 & 97.70 & 94.81 & 96.97 & 97.33 \\ Ours & 91.53 & 87.16 & 95.37 & 93.14 & 98.85 & 96.59 & 97.69 & 98.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of water detection results among various networks in Scene II. exhibit good detection accuracy in the detection of large water bodies. The PA values of U-Net and GCAFF-Net are 92.65% and 91.53%, respectively. However, U-Net generated a large number of false positives, resulting in its IoU being only 80.72%, its Recall being 83.12%, and its F1-Score being 89.34%. These key performance indicators are far lower than the corresponding values of GCAFF-Net, which are 87.10%, 95.37%, and 93.14%, respectively. This result highlights the progressiveness of the GCASPP in global semantic information extraction. As shown in Table 4, in scenarios featuring irregular water body shapes, complex backgrounds, and rich detailed characteristics, GCAFF-Net outperforms the comparison networks in various evaluation metrics for water body extraction. This result indicates that the effective fusion of multi-level features achieved through the AFFM module not only significantly enhances the network's ability to suppress background noise but also ensures a stable extraction performance under different water body characteristics, effectively avoiding the loss of feature information. As a backbone network, ResNet101, despite its higher computational complexity and a parameter count of 44.5 million, demonstrates significant advantages in high-precision segmentation tasks, attributed to its deep residual structure and robust feature extraction capabilities.