## 3 Results
### Comparison Experiments
#### 3.2.1 Scene I  
In Scene I, the edge of the water body exhibits unevenness, characterized by a rough and varied contour. This irregularity poses challenges in accurately delineating the water body for detection purposes. According to the water body detection results in Figure 5, several algorithms can basically detect the large area of the water body correctly, while there are some false alarms and missed detections in other small water body parts and water body edges. GCAFF-Net extracts water boundaries more clearly than Deeplabv3+, MF2 AM, and U-Net. The water in Scene I has irregular small boundaries, and DeepLabV3+ and U-Net have obvious missed detections with relatively few false alarms. The water extraction of Deeplabv3+ and U-Net is not precise enough to extract the small boundary areas at the edge of the water, resulting in a large number of missed detection areas. MF2 AM has fewer missed detections, but there are more false alarms when judging the edge area of the water body. The SCAFF-Net proposed in this article significantly reduces missed detections compared to DeepLabV3+ and U-Net, and also has much fewer false alarms than MF2 AM, achieving very good detection results. According to Table 1, the detection accuracy of this network is much higher than other networks. The network accuracy of U-Net is only 67.25%, with a large number of missed detections. Although the Deeplabv3+ achieved a recall value of 97.96%, indicating that it can recognize and label most of the water body areas, its PA was only 83.14% and its IoU value was only 81.73%. These two indicators are relatively low, indicating that the Deeplabv3+ has problems related to inaccurate edge segmentation and region recognition errors when dealing with water scenes with irregular edges and an uneven distribution. The detection accuracy of the SCAFF-Net network in this paper reached 95.24%, showing significant advantages compared to other networks. SCAFF-Net has a strong ability to express the semantic features of water bodies, which can refine the edge contours of water bodies and more accurately extract boundary information. It has obvious advantages in boundary pixel classification. To fully demonstrate the superiority of our proposed network in extracting detailed water body edges and small pond water bodies, we present the zoomed-in views of the water body detection results for Scene I, as shown in Figure 6. In this scene, the following observations can be clearly made: U-Net and Deeplabv3+ exhibit substantial  
Figure 5: The detection results for water bodies in Scene I using various networks. (**a**) The first SAR image for independent testing. (**b**) The ground truth. (**c**) The extracted water result for U-Net; (**d**) for MF2 AM; (**e**) for DeepLabV3+; and (**f**) for SCAFF-Net. The blue color indicates correct detections of water, the green color highlights missed detections, and the red color marks false alarms. missed detections along the water edges; MF2 AM generates numerous false alarms along the water edges; in contrast, SCAFF-Net demonstrates the lowest number of both false alarms and missed detections along the water edges. Furthermore, SCAFF-Net successfully detects small water bodies that other models fail to identify. Overall, compared to other methods, SCAFF-Net exhibits the most complete extraction of water body edges and the highest preservation of water details. As illustrated in Table 2, SCAFF-Net attains an IoU value of 92.25%, while the IoU values of the other networks are all below 90%. It is noteworthy that, despite MF2 AM having the highest PA value, its IoU value is 3.57% lower than that of SCAFF-Net, indicating that Deeplabv3+ lacks in detail capture. Additionally, our SCAFF-Net boasts the highest F1-Score of 95.97%, demonstrating a well-balanced performance between Precision and Recall. In summary, SCAFF-Net demonstrates the best performance in extracting detailed water body edges and small pond water bodies.