# Optimization of DG-LRG Water Extraction Algorithm Considering Polarization and Texture Information
## 5 Conclusions  
This article proposed an optimized local region growth algorithm that takes into account the polarization mode and texture features of SAR images for rapid water body extraction. An experiment was conducted with Fangshan District as the research object, and the main conclusions are as follows.  
Compared with traditional threshold segmentation methods, the DG-LRG method proposed in this study can obtain more effective water body information, with a recall rate of 83.24% and a precision rate of 89.87% for water bodies in the study area. In terms of the F1 Measure, the DG-LRG method yielded a 4.5% improvement over the Otsu threshold segmentation method.  
Optimizing the DG-LRG results based on morphological and external DEM methods allowed for more accurate water body information to be effectively obtained. These advancements highlight their practical value for rapid flood emergency monitoring, as evidenced by the successful identification of 88 transmission towers at risk with 94.32% precision. However, the algorithm's performance remains constrained by its dependency on seed point selection and the trade-off between computational efficiency and local optimization granularity. For instance, fragmented water bodies in narrow river channels may be partially omitted due to sparse seed distributions. Future research should explore hybrid strategies combining DG-LRG with edge-aware deep learning models to recover omitted water pixels while maintaining high precision.  
From the results of emergency flood disaster monitoring examples, the proposed method was shown to be capable of rapidly and accurately extracting the water body range in the experimental area and, therefore, has good applicability and timeliness in the context of large-scale flood emergency monitoring. The proposed method can help to obtain detailed information for the affected area in a timely and accurate manner, providing reference for emergency rescue and disaster relief services, as well as providing support for flood control dispatch and transmission line decision making. Future enhancements, such as optimizing the PSO convergence process and validating the framework with multi-sensor SAR data, are expected to improve its robustness across diverse geographic and climatic  
Figure 9: Distribution Map of Affected Transmission Towers in Fangshan District. The blue region and the red region are same as in Figure 8. The orange points, yellow points and green points represent High risk towers, Medium risk towers and Low risk towers, respectively.  
conditions. These advancements will further bridge the gap between methodological innovation and operational demands in global flood disaster management.  
Conceptualization, L.T. and Y.L.; methodology, L.T. and R.Y.; formal analysis, L.T., Y.L. and R.Y.; investigation, K.Z.; resources, K.Z. and R.Z.; data curation, R.Z.; writing--original draft preparation, L.T. and R.Y.; writing--review and editing, Y.L. and J.L.; visualization, R.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The GaoFen-3 SAR images can be downloaded from the website: [[https://data.cresda.cn](https://data.cresda.cn)]([https://data.cresda.cn](https://data.cresda.cn))(accessed on 1 August 2023).  
We would like to thank the China Resources Satellite Center for providing the GF-3 data. We also truly appreciate anonymous reviewers for their useful and constructive suggestions and comments for manuscript improvement.  
Authors [PERSON] and [PERSON] were employed by the company Beijing Deep Blue Space Remote Sensing Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.