## 4 Conclusions  
In this paper, a deep learning semantic segmentation network, namely GCAFF-Net, is proposed for water extraction from SAR images, addressing the insufficient ability to \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Network** & **ResNet** & **GCASPP** & **AFFM** & **AMM** & **PA (\%)** & **IoU (\%)** \\ \hline \multirow{4}{*}{Deeplabv3+} & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & 84.67 & 79.81 \\  & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & 87.69 & 84.43 \\ \cline{1-1}  & \(\surd\) & \(\times\) & \(\surd\) & \(\times\) & 88.15 & 85.57 \\ \cline{1-1}  & \(\surd\) & \(\times\) & \(\times\) & \(\surd\) & 87.46 & 85.73 \\ \cline{1-1}  & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 92.12 & 86.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Assessment of ablation study results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scene** & **Method** & **PA (\%)** & **IoU (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline \multirow{4}{*}{Scene I} & GCAFF-ResNet & 95.24 & 91.63 & 96.03 & 95.63 \\  & GCAFF-MobileNetV2 & 94.01 & 87.16 & 92.29 & 93.14 \\ \hline \multirow{4}{*}{Scene II} & GCAFF-ResNet & 91.53 & 87.16 & 95.37 & 93.14 \\  & GCAFF-MobileNetV2 & 91.40 & 85.97 & 93.22 & 92.45 \\ \hline \multirow{4}{*}{Scene III} & GCAFF-ResNet & 89.59 & 82.10 & 92.93 & 90.17 \\ \cline{1-1}  & GCAFF-MobileNetV2 & 85.54 & 79.47 & 91.81 & 88.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of water detection results among various backbone networks. extract water edge details, the inability to detect small water bodies, and the weak ability to suppress background noise. It integrates global and local information, and can accurately refine the edge contour of water body, demonstrating excellent feature extraction and segmentation performance. The GCAFF-Net model consists of two modules: an encoder module for hierarchical feature extraction and a decoder module for fusing multi-scale features. The encoder module is responsible for the in-depth feature extraction of the input SAR image. The AFFM and GCASPP enable the model to capture more extensive and in-depth information, laying the foundation for subsequent segmentation tasks. The decoder module integrates AMM to rearrange the distribution of feature importance from the channel space sequence perspective, so as to better extract the detailed features of the water body, thereby significantly improving the ability to detect water bodies. On the self-annotated dedicated dataset, the detection accuracy of the GCAFF-Net reached 95.24%, and the intersection to union ratio also reached 91.63%, achieving a significantly better water detection performance than several other excellent classification networks. Although GCAFF-Net achieved better results on the current dataset, there is still potential for a further improvement in its network performance. Facing these challenges, future research directions will concentrate on how to further optimize and augment the model's capabilities. In our future work, we will continue to explore and research techniques for reducing speckle noise, attempting to integrate other advanced technologies to further improve the accuracy and robustness of water body extraction from SAR images. Furthermore, we intend to conduct in-depth research on how to further optimize the computational efficiency while maintaining a high performance, and explore more advanced lightweight technologies. We plan to expand the dataset by introducing more SAR images from other bands, different resolutions, and multiple sources to increase its diversity and coverage. This measure is crucial for improving the generalization performance of the model in different water backgrounds, and can help the model better adapt to various complex water environments.