# Optimization of DG-LRG Water Extraction Algorithm Considering Polarization and Texture Information
###### 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.  
flood monitoring; spaceborne SAR; region growth algorithm; water extraction; transmission tower 2025 15 4434 +
Footnote †: journal: Applied sciences  
0000-0001-939X/0000-0002-289X/0000-0000-0000-0000-0000-0000-0000-0000-00000-00000-0000  
variance method [6], water index extraction method [7], and regional growth algorithm [8]. With the rapid development of machine learning in recent years, relevant approaches have gradually become widely used for water body recognition, including convolutional neural networks (CNNs) [9; 10; 11; 12], random forests (RFs) [13], support vector machines (SVMs) [14; 15], and other methods. Although machine learning algorithms have high recognition accuracy, their results are limited by the selection of features, and the process of training samples and models is associated with high time and cost requirements, making it difficult to meet the needs of large-scale flood emergency monitoring. The advantage of threshold methods is that they are simple, fast, and can adequately meet the requirements of emergency response. Despite this efficiency, threshold methods suffer from two unresolved challenges: (1) limited discriminative power: on-water features (e.g., mountain shadows, paved roads) with backscatter coefficients overlapping water bodies are frequently misclassified; (2) global threshold bias: a single threshold fails to account for local variations caused by window-roughened water surfaces or submerged vegetation. Thus, it is necessary to utilize other methods in combination, such as contour modeling, morphology, and texture information, in order to improve monitoring accuracy [16].  
Based on this, we propose a Dual Polarized and GLCM in Local Region Growth (DG-LRG) algorithm that takes into account the polarization mode and SAR image texture features, combining traditional region growth algorithms with the polarization mode, texture features, optimization algorithms, morphological methods, and external geographic information. The primary objective is to enable rapid, high-precision flood mapping without requiring training data. Unlike threshold methods relying solely on backscatter intensity, DG-LRG takes dual-polarized SAR data (HH/HV) and multi-scale texture features as primary inputs. The polarization modes enhance separability between water and specular surfaces, while texture statistics (e.g., homogeneity, contrast) extracted from GLCM suppress false alarms in heterogeneous regions. In this way, the flood range of Fangshan District is extracted, which was the most severely affected area in the \"7.31\" flood disaster in Beijing.