DMLU-Net: A Hybrid Neural Network for Water Body Extraction from Remote Sensing Images

• Published in: Applied sciences Vol. 15; no. 14; p. 7733
• Main Authors: Xu, Ziqiang, Li, Mingfeng, Guo, Haixiang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2025
• Subjects: Accuracy; Analysis; Artificial satellites in remote sensing; Climate change; CNN; Computational linguistics; Datasets; Deep learning; Environmental monitoring; Language processing; Liu, Timothy; Machine learning; Natural language interfaces; Neural networks; Performance evaluation; Remote sensing; remote sensing image; Scale models; Semantics; U-Net; water extraction; Water resources; China

The delineation of aquatic features from satellite remote sensing data is vital for environmental monitoring and disaster early warning. However, existing water body detection models struggle with cross-scale feature extraction, often failing to resolve blurred boundaries, and they under-detect smal...