# A Novel Deep Learning Network Model for Extracting Lake Water Bodies from Remote Sensing Images
## 3 Results and Discussion
### Model Performance Comparison
#### 3.4.1 Comparison of Model Visual Observations  
In this paper, visual observations are made to analyze the performance of the R50A3-LWBENet model in dealing with three challenges, which are multi-scale features, noise interference and boundary blurring. 1. Performance comparison for multi-scale features. As the resolution of remote sensing satellites gradually increases, more and more details in remote sensing images become visible. The lake water bodies in Figure 7 are of varying sizes and shapes, and there are multi-scale features, such as large differences in the size of the lake water bodies in columns (a) and (b), and multiple smaller water bodies in columns (c), (d) and (e). The experimental results show that only R50A3-LWBENet and MU-Net accurately extracted the small water body region and also completely segmented the large water body region, U-Net and RU-Net water body extraction results are more fragmented, and both AU-Net and ARU-Net failed to segment the small water body to varying degrees. SER34 AUNet missed two small bodies of water. The R50A3-LWBENet model can take into account different levels of contextual information, automatically learn multi-scale features, and extract more accurate and complete contours of lakes and water bodies of different types and complex geographic environments, with strong generalization ability. 2. Performance comparison for noise interference. In Figure 8, the image of column (a) was taken in winter, and the frozen lake and snow appeared, and the spectral features of the two were very similar, and the color of the lake water body in column (b) was dark green, and the difference with the mountains and  
Figure 7: Performance comparison of different models for multi-scale features. (**a**–**e**) represent 5 images of lake areas containing multi-scale features. The red circles represent key segmentation areas. R50A3-LWBENet is the water body segmentation model proposed in this paper. shadows next to it was small, except for R50A3-LWBENet, the rest of the models were not able to inhibit the noise interference well, and failed to extract the detailed boundaries of the lake water body, and all of them appeared to identify the irrelevant objects such as the snow, the mountain range, and the shadows as the information of the water body. The color of the lake water body in column (c) is light blue, the environment around the lake is complex, there is a salt belt along the shore, the texture is rich, and the segmentation is easy to generate noise, only R50A3-LWBENet, ARU-Net, SER34 AUNet, and MU-Net can handle the complex area well, and completely and accurately segmented the lake water body and the land, while the performance of U-Net, AU-Net, and RU-Net is not satisfactory enough. The lake water body in column (d) has grayish-white frozen blocks at the edge and a large cloud cover layer. U-Net, AU-Net, RU-Net, ARU-Net, and SER34 AUNet suffer from large mis-extraction of water body extraction due to cloud interference, while R50A3-LWBENet and MU-Net are able to adequately extract the water body features. The image of the water body in column (e) shows green color, and its spectral properties and the spectral properties of the background features are obviously different, but both U-Net and RU-Net more or less mistook the snow and shadows as lakes. AU-Net, ARU-Net and SER34 AUNet outperformed U-Net and RU-Net, but also showed minor mis-segmentation, R50A3-LWBENet and MU-Net had the highest accuracy for water body extraction without any noise points, and R50A3-LWBENet was robust to noise interference. Figure 8: Performance comparison of different models for noise interference. (**a**–**e**) represent 5 images of lake areas containing noise. The red circles represent key segmentation areas. R50A3-LWBENet is the water body segmentation model proposed in this paper. (3) Performance comparison for boundary blurring.