# Optimization of DG-LRG Water Extraction Algorithm Considering Polarization and Texture Information
## 4 Results and Discussion
### Evaluation of Water Extraction Accuracy  
Due to the time required for the flood to recede, a 2 m resolution optical image without cloud cover for Fangshan District, on August 3\({}^{\text{rd}}\), was selected for high-precision identification of water bodies. In terms of cloud coverage, the selected high-resolution optical imagery exhibits <5% cloud cover, with cloud-contaminated pixels masked out using 10-m resolution Sentinel-2 data. For water body extraction, the process was conducted by three experienced remote sensing image analysts, achieving 95% inter-annotator agreement. The recognition results are shown in Figure 7.  
This result was used as reference for the accuracy evaluation. Compared with the water body results extracted by GF-3, the three indicators of Recall, Precision, and False Alarm Rate (FAR) were used for accuracy evaluation of the DG-LRG method. In particular, Recall is the proportion of correctly detected positive cases among all actual positive cases, which can measure a model's ability to recognize positive cases; Precision is the proportion of correctly detected positive cases among all positive cases detected by the model, which can measure the accuracy of the model; and the FAR is the proportion of negative cases detected as positive cases in actual negative cases, which can measure the degree of false alarms in the model [24,25]. Table 3 shows the accuracy evaluation statistical results for the Otsu threshold method, DG-LRG method, and optimized DG-LRG method. Figure 6: Comparison of Water Extraction Results for River Regions: (**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 external DEM data. It can be seen that the Otsu threshold method had the highest recall rate and could identify a large number of water bodies. However, due to the influence of noise and mountain shadows in the study area, its precision rate was only 77.65%. Although the recall rate of the DG-LRG method proposed in this article was slightly lower than that of the Otsu threshold method, its precision rate was significantly improved, reaching 89.87%. Comparing the F1 index calculated using the comprehensive recall rate and precision rate, the DG-LRG method showed an improvement of 4.5% when compared to the Otsu threshold method. After morphological and external DEM optimization, the F1 index of DG-LRG further improved. The trade-off between precision and recall in DG-LRG versus Otsu can be attributed to fundamental differences in their operational principles. The reasons for the Recall reduction in DG-LGR may be that region growth algorithms rely on seed points to initiate segmentation. If seeds are sparsely distributed in complex water bodies (e.g., fragmented lakes or narrow rivers), partial water areas may be omitted, reducing recall, while Otsu's global threshold captures all potential water pixels (including noise). Meanwhile, the reasons for the Precision improvement in DG-LGR may be that the integration of GLCM texture features (homogeneity, contrast) and dual-polarization (HH/HV) information enables DG-LGR to effectively suppress false alarms from spectrally similar non-water features (e.g., smooth roads, mountain shadows). Closing operations fill voids within water bodies, while DEM-based shadow removal eliminates misclassified pixels, further enhancing precision. Optimized DG-LRG's precision (90.76%) ensures reliable identification of true flood extents, even at the cost of minor under-detection. In \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Recall/\%** & **Precision/\%** & **FAR/\%** & **F1 Measures/\%** \\ \hline Otsu & 86.71 & 77.65 & 22.35 & 81.93 \\ DG–LRG & 83.24 & 89.87 & 10.13 & 86.43 \\ Optimized DG–LRG & 84.13 & 90.76 & 9.24 & 87.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical comparison of water extraction accuracy. Figure 7: Optical image water body recognition results.