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
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app15147733

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 small water bodies in complex landscapes. To tackle these challenges, in this study, we present DMLU-Net, a U-shaped neural network integrated with a dynamic multi-kernel large-scale attention mechanism. The model employs a dynamic multi-kernel large-scale attention module (DMLKA) to enhance cross-scale feature capture; a spectral–spatial attention module (SSAM) in the decoder to boost water region sensitivity; and a dynamic upsampling module (DySample) in the encoder to restore image details. DMLU-Net and six models are tested and compared on two publicly available Chinese remote sensing datasets. The results show that the F1-scores of DMLU-net on the two datasets are 94.50% and 86.86%, and the IoU (Intersection over Union) values are 90.46% and 77.74%, both demonstrating the best performance. Notably, the model significantly reduces water boundary artifacts, and it improves overall prediction accuracy and small water body recognition, thus verifying its generalization ability and practical application potential in real-world scenarios.