- Research Article
1
- 10.1109/jstars.2025.3631633
OF-PhyNet: A Hybrid Neural Network Method for River Surface Velocity Measurement Based on Optical Flow and Physical Constraints
- Jan 01, 2026
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Minghu Zhang + 3 more +3
Measuring river surface velocity is crucial for hydrological monitoring. However, traditional methods exhibit significant limitations under extreme weather conditions and emergency scenarios. In this study, we propose OF-PhyNet, an innovative hybrid neural network architecture designed as an image-based velocimetry method to improve the accuracy of river surface velocity estimation. By integrating an optical flow algorithm with a physics-informed neural network, OF-PhyNet achieves deep coupling of motion feature extraction with fluid dynamics constraints. To address the challenges posed by uneven particle distribution and illumination variations in natural rivers, we introduce a feature extraction strategy based on K-means clustering and morphological operations, which enables effective flow field segmentation and reliable identification of particle regions. Experimental validation conducted in the Heihe River Basin, using Stalker Pro II surface velocity radar (SVR) as a reference, demonstrates that OF-PhyNet achieves a mean absolute error of 0.054 m/s and a relative error of 7.22% across 12 cross-sectional measurement points, substantially outperforming PIVLab with 11.65% and the Horn-Schunck algorithm with 25.84% relative error. As a UAV-based image velocimetry approach, OF-PhyNet offers exceptional mobility for rapid flood monitoring and enables robust reconstruction of flow fields in particle-sparse regions, delivering precise non-contact river velocity measurements beyond the reach of traditional methods.
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