收稿：2025-09-12，
纸质出版：2026-04-07
移动端阅览
时序InSAR技术因具备全天候、全天时、高精度和高空间分辨率等优势，已广泛应用于桥梁结构健康监测。但在大型桥梁监测中，大量伸缩缝和显著的热胀冷缩效应会导致相位跳变，破坏传统解缠方法所依赖的空间连续性假设，从而难以准确反演形变。本文介绍了一种无需先验模型假设的自适应分段解缠时序InSAR算法：通过设定弧段误差阈值自动识别伸缩缝处的相位突变并实现网络分割，结合桥梁力学特性完成子网参考点选取和分段解缠，进而恢复相干点位移序列。以杭州湾跨海大桥为研究对象，利用13景PAZ X波段卫星影像对方法进行了验证。结果表明该方法能有效定位伸缩缝并实现稳定解缠，其适应性和稳健性优于最小费用流（MCF）算法。进一步分析发现，桥梁整体形变与温度高度相关（最高相关系数0.988）；在扣除热效应后，97.1%的相干点残余形变速率小于2 mm/a，仅个别路段呈现潜在非热源性变形。研究成果为大型桥梁健康监测提供了新的思路，有助于提升时序InSAR在基础设施监测中的可靠性与实用性。
Time-series Interferometric Synthetic Aperture Radar (InSAR)
which has the advantage of an all-weather
24-hour operation with high precision and high spatial resolution
has been widely applied to bridge structural health monitoring. However
numerous expansion joints and pronounced thermal expansion and contraction effects identified during large-scale bridge monitoring often cause phase discontinuities. This limitation undermines the spatial continuity assumption required by conventional phase unwrapping methods and hinders accurate deformation inversion. This paper presents an adaptive segmented unwrapping time-series InSAR algorithm that does not rely on prior model assumptions. Phase discontinuities at expansion joints and the interferometric network partitioned by setting arc error thresholds are automatically identified. Then
subnet reference points are selected based on bridge mechanical characteristics to enable segmented unwrapping
thereby recovering the displacement time series of coherent points. The method was validated using 13 scenes of PAZ X-band satellite imagery over the Hangzhou Bay Bridge. Results show that the proposed approach effectively locates expansion joints and achieves stable unwrapping
with better adaptability and robustness than the minimum cost flow algorithm. Further analysis reveals that the overall bridge deformation is highly correlated with temperature (maximum correlation coefficient of 0.988). After the thermal effects are removed
97.1% of coherent points exhibit residual deformation rates below 2 mm/year
with only a few sections showing potential nonthermal deformation. These findings provide new insights for the health monitoring of large bridges and contribute to enhancing the reliability and applicability of time-series InSAR in infrastructure monitoring.
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