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High-Resolution Remote Sensing Imagery Water Body Extraction Using a U-Net with Cross-Layer Multi-Scale Attention
Chunyan Huang1, Mingyang Wang1, Zichao Zhu1
1School of Mathematics and Statistics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China.
Sensors (Basel, Switzerland)
|September 27, 2025
Summary
Accurate water body extraction from remote sensing is improved by AMU-Net. This novel deep learning model enhances feature recognition and boundary delineation for better water resource monitoring and flood warnings.
Area of Science:
- Environmental Science
- Computer Science
- Remote Sensing Technology
Background:
- Accurate water body extraction from remote sensing data is vital for water resource management and disaster response.
- Challenges include complex land cover, varied water body morphology, and spectral similarities, leading to low accuracy in traditional methods.
- Existing deep learning models, like convolutional neural networks (CNNs), struggle with multi-scale features and global context for water segmentation.
Purpose of the Study:
- To develop an advanced deep learning model, AMU-Net, for precise water body extraction from remote sensing imagery.
- To enhance feature recognition, boundary delineation, and overall segmentation accuracy in challenging environments.
- To provide an effective solution for water resource monitoring and flood disaster warning systems.
Main Methods:
- Proposed AMU-Net, a U-Net based model incorporating an improved residual connection module for feature learning.
- Introduced a multi-scale attention mechanism with grouped channel attention and multi-scale convolutions for efficient segmentation.
- Employed a dual-attention gated modulation module and a cross-layer geometric attention fusion module for boundary localization and accuracy.
- Utilized a triple-constraint loss framework to optimize global classification, regional overlap, and background specificity.
Main Results:
- AMU-Net achieved high Intersection over Union (IoU) scores: 93.6% on the GID dataset and 95.02% on the WHDLD dataset.
- The model demonstrated superior performance in recognizing water features and delineating boundaries compared to existing methods.
- Enhanced feature learning, multi-scale representation, and attention mechanisms contributed to improved segmentation accuracy.
Conclusions:
- AMU-Net offers a significant advancement in remote sensing water body extraction, addressing limitations of traditional CNNs.
- The proposed model provides a robust and accurate solution for critical applications like water resource management and flood prediction.
- Future work may involve further refinement of attention mechanisms and loss functions for even greater precision.
