Work

A Novel Method for Monitoring River Level Changes Under Bridges With Time Series SAR Images

Year: 2025

Type: article

Abstract: River level monitoring is crucial for hydrological studies, providing essential information for flood forecasting, water resource management, and environmental protection. In this paper, we present a novel method for monitoring river level under bridges using time-series SAR images. Firstly, we transfer a DeepLab V3+ network model for road segmentation to bridge segmentation, fine-tuning it with bridge scattering signal data, while a new loss supervision function CentroidLoss, has been added to the model to improve the integrity of the bridge signal segmentation. Furthermore, the Energy Accumulation Algorithm (EAA) is proposed to improve the accuracy of river level measurements in areas of low signal-to-noise ratio with noise such as ships and waves. Leveraging deep learning and energy accumulation algorithm, the proposed approach accurately extracts bridge scattering signals and precisely estimates the peak positions of the bridge's multiple scattering signals, enabling precise river level monitoring. Sentinel-1A and COSMO-SkyMed data were applied as inputs to our method, and the comparison between the river levels measured by the proposed method and those of local hydrological stations reveals sub-meter level estimation accuracy.

Source: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

DOI: 10.1109/jstars.2025.3579775

Authors: Yifan Wang, Mofan Li, Gen Li, Zihan Hu, Zehua Dong, Han Li

Institution: Beijing Institute of Technology

Language: English

Topic: Synthetic Aperture Radar (SAR) Applications and Techniques

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Clean water and sanitation

Open Access status: gold

Estimated APC paid: $1,800