# Optimization of DG-LRG Water Extraction Algorithm Considering Polarization and Texture Information
## 4 Results and Discussion  
### Flood Range Extraction Results  
To verify the effectiveness of the method proposed in this article, typical water bodies such as lakes, mountain water bodies, and river channels were selected for detailed analysis within the study area. The Otsu threshold segmentation results, DG-LRG method extraction results, and the optimization results were compared using morphological and external DEM data.  
Figure 4 shows the extraction results for a lake area. It can be seen that, in the intensity map, some areas within the lake have complex water environments, and there are differences in pixel grayscale compared to other water bodies. Therefore, in the Otsu threshold segmentation results, some water bodies are missing, and there is obvious noise throughout the entire area, leading to many omissions and misclassifications in the Otsu results. In the results of the DG-LRG method proposed in this article, the noise interference was significantly reduced and the water area was relatively complete. However, there were still some isolated pixels forming voids, and the extraction of some small areas was incomplete. In comparison, the optimized results were more complete and smoother, with a significant reduction in internal voids and fewer misclassifications and omissions.  
Figure 5 shows the extraction results for a mountain water body. It can be seen that, due to the presence of mountain shadows, there were a large number of misclassifications in the Otsu threshold segmentation results, and the river was not continuous, showing an intermittent state. In the DG-LRG results, the problem of mountain shadows was largely solved; however, many mountain shadows adjacent to rivers were also shown as eligible areas, resulting in misclassification. From the results of the optimized model, it can be seen that, after external DEM optimization, shadows adjacent to water bodies were basically eliminated. However, there was also a certain deviation between the results and the actual situation and, due to differences in registration accuracy and resolution, there may be errors, such as the removal of rivers. However, compared to the results before optimization, the accuracy was significantly improved.  
Figure 4: Comparison of Water Extraction Results for Lake Regions (**a**) is a back-scatter intensity map; (**b**) is the result of Otsu; (**c**) is the result of DGL–RG; (**d**) is the result of optimized DG–LRG by morphological methods and with external DEM data.  
Figure 5: Comparison of Water Extraction Results for Mountain Water Bodies: (**a**) is a back-scatter intensity map; (**b**) is the result of Otsu; (**c**) is the result of DG–LRG; (**d**) is the result of optimized DG–LRG by morphological methods and with external DEM data.  
Figure 6 shows the extraction results for a river channel area. Through visual interpretation of the optical image, it can be seen that the part that intersects with the river is a road. In the Otsu threshold segmentation results, some roads were mistakenly classified as water bodies, and the river itself appears discontinuous, with obvious noise effects around the river channel. This noise is significantly reduced in the DG-LRG results, the river channel is more complete, and the situation of roads being misclassified is also improved. The effect of the optimized model was not as obvious in this situation, mainly manifested as a reduction in internal voids in the river, smoother edges, and the result were more in line with human visual characteristics.