Work

Optimization of DG-LRG Water Extraction Algorithm Considering Polarization and Texture Information

Year: 2025

Type: article

Abstract: Flooding is one of the most frequent natural disasters at present, and can pose a serious threat to transmission towers. In response to the accuracy and timeliness requirements of flood emergency monitoring, a local region growth algorithm combining polarization and texture information is proposed for Synthetic Aperture Radar (SAR) image water recognition. Morphological methods and external geographic information are used to optimize the results, allowing for rapid extraction of the flood range. The method is validated using Gaofen-3 (GF-3) Fine Strip Imaging Mode II (FSII) SAR images covering Fangshan District in Beijing, China. The experimental results indicate that this method can obtain more effective water information compared to traditional threshold segmentation methods, and can also reduce the effects of noise and mountain shadows. It has good applicability and timeliness with respect to large-scale flood emergency disaster monitoring, and can help to rapidly and accurately obtain detailed information of flood-affected areas, thus providing reference for emergency rescue and disaster relief services.

Source: Applied Sciences

DOI: 10.3390/app15084434

Authors: Lei Tan, Yunpeng Liu, Kai Zhou, Ruizhe Zhang, Jintian Li, Ruopeng Yan

Institutions: North China Electric Power University, State Grid Corporation of China (China)

Language: English

Topic: Flood Risk Assessment and Management

Subfield: Global and Planetary Change

Field: Environmental Science

Domain: Physical Sciences

SDG: Clean water and sanitation

Open Access status: gold

Estimated APC paid: $2,896