On the Suitability of Distributed Scatterers for Bridge Monitoring in Very High Resolution SAR Data

Application of satellite for geotechnical monitoring of high-speed railway subgrade settlement: a comparative study of PS-InSAR and ComSAR

Risk evaluation of transportation network system with spaceborne synthetic aperture radar technology

Spaceborne Synthetic Aperture Radar (SAR) is a non-contact remote sensing technology that detects surface deformation by analyzing the phase differences between radar images acquired over the same area at different times. Due to its extensive coverage, high spatial resolution, and all-weather operational capability, spaceborne SAR has become an established technique for large-scale, continuous monitoring of civil infrastructure. Transportation networks constitute a fundamental component of urban infrastructure, playing a pivotal role in enabling efficient mobility and fostering regional economic development. Extreme weather events severely threaten the durability and operational safety of transportation networks. However, limited funding restricts the deployment of traditional sensors for detailed and comprehensive monitoring of the entire transportation network system. In this research, a stack of Sentinel SAR images acquired over a two-years period is collected from the Copernicus Data Space Ecosystem, and subsequently processed with Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technology. A dedicated post-processing procedure, consisting of PS points refinement and clustering analysis, is applied to the displacement time series derived from the PS-InSAR processing. Then, statistical control limits method is employed to evaluate the risk levels across transportation network. Finally, the reliability and effectiveness of the proposed risk assessment framework are validated through a specific bridge case study. These findings demonstrate the potential of the proposed framework for large-scale, risk-informed assessment of transportation networks, thereby contributing to more proactive and data-driven transportation network management strategies.

Spatio-temporal modeling of bridge deformations from Sentinel-1 SAR images validated with multiple in-situ surveys

Abstract. Aging bridge infrastructure requires efficient, network-scale monitoring, especially in remote areas where traditional in-situ sensors are costly and logistically challenging. This paper presents a remote sensing framework for structural health monitoring based on spaceborne Synthetic Aperture Radar (SAR). The approach combines Persistent Scatterer Interferometry (PSI) and Least Squares Collocation (LSC), implemented through the PHASE open-source MATLAB software, to derive a millimeter-level spatio-temporal displacement model. The methodology is applied to a reinforced-concrete viaduct in the Alpine foothills of Lombardy, Italy, using five years of Copernicus Sentinel-1 data. A custom elevation-based spatial filtering strategy enables the isolation of structural displacements from the surrounding topography. The resulting spatio-temporal displacement model captures the expected seasonal thermal behavior of the structure and highlights localized deviations from the dominant cyclic response. Finally, the SAR-derived model is integrated with UAV photogrammetry and official inspection reports within the P.O.N.T.I. 3D viewer. This multi-source, Digital Twin-like environment facilitates the joint interpretation of remote sensing observations and in-situ evidence, providing a scalable framework to support infrastructure monitoring and management.

Assessment of railway transition zone settlements using InSAR – A comparison between Sentinel-1, TerraSAR-X and track recording car data

Satellite-based remote sensing tools such as Synthetic Aperture Radar (SAR) Interferometry (InSAR) has emerged as a potential tool for condition monitoring of railway track and infrastructure. This is due to its frequent and stable collection of data without requiring personnel or capacity occupation on the railway track. SAR is an active radar, meaning that it can collect images during cloud coverage and do not rely on sunlight. It collects scattered reflections as complex images with pixel intensity and phase. Current satellites instrumented with SAR have a passing frequency over a location on the earth at best 6 days. Interferometry exploits the phase difference between two or more SAR images collected at either different positions or times, which effectively can give information about the relative difference in distance between the images. Differential InSAR (D-InSAR) considers SAR images of the same location at different times, resulting in measurements of ground motion in the satellite line of sight. The system design of the radar used for SAR has implications on the spatial resolution of the earth surface measurement, which is generally inversely proportional to spatial coverage. The aim of this paper is to assess the applicability of Sentinel-1 and TerraSAR-X data for track irregularity monitoring considering their different system design of the radar. Sentinel-1 has wider coverage and lower resolution compared to TerraSAR-X. In contrast, Sentinel-1 satellite data is open to global users. Exploring its possibilities and limitations for railway maintenance supports the development of more robust and cost-efficient maintenance decisions. The case study for this assessment is a transition zone between railway bridge and ballasted track in Sweden. Transition zones are interesting objects of study from a condition monitoring point of view as they often exhibit differential settlements and therefore worse track geometry. This is because of differences in settlement resistance for the different types of track structure that meet in the transition zone. The reference geometry data for the transition zone consists of measured track irregularities from chord-based track monitoring vehicles which are used to assess railway longitudinal levels (vertical track irregularities) at three different wavelength ranges, D1 = 3-25 m, D2 = 25-70m, and D3 = 70-150m. Shorter wavelength longitudinal level variations is associated with safety levels and is the basis for track maintenance in Sweden, whereas the longer wavelengths are associated with ride comfort.

Snow Cover Classification Using High-Resolution Reconstructed FY-3E WindRAD Data

Microwave scatterometers are capable of acquiring land surface backscattering coefficients day and night under all-weather conditions, offering advantages for snow cover monitoring. However, the relatively low spatial resolution of traditional scatterometer data limits their application in the fine-scale monitoring of snow cover distribution. To improve the spatial representation of snow cover and mitigate mixed-pixel effects in complex spring snowmelt scenarios, this study proposes an adaptive bilateral filtering scatterometer image reconstruction (SIR-ABF) algorithm based on the rotating fan-beam scanning characteristics of the FengYun-3E Wind Radar (FY-3E WindRAD). The Ku-band data of FY-3E WindRAD were reconstructed from the original 10 km resolution to an enhanced resolution of 3.125 km. Furthermore, by integrating the reconstructed scatterometer backscatter with multi-source auxiliary data, an optimal feature subset was determined through a feature selection strategy that considers both feature-label correlation and inter-feature multicollinearity. Finally, the best feature subset was combined with four machine learning (ML) models for snow cover classification. The results indicate that the Support Vector Machine (SVM) achieved the best performance, yielding an Overall Accuracy (OA), Macro-F1, and Kappa coefficient (Kc) of 91.64%, 86.47%, and 0.730, respectively. Compared with the snow cover classification results derived from the original 10 km Ku-band data, the 3.125 km reconstructed data provided more detailed spatial information and better characterized fragmented snow patches and snow transition boundaries. Further comparison with existing snow cover products demonstrated the spatial consistency and continuity of the proposed classification results, highlighting the potential of high-resolution scatterometer data for fine-scale snow cover monitoring during the spring snowmelt period in Northeast China.

A physics-based framework for enhancing PSI bridge monitoring

Remote sensing techniques, particularly Interferometric Synthetic Aperture Radar (InSAR), offer a promising, cost-effective solution for civil infrastructure monitoring without the need for on-site instrumentation. Among InSAR methods, Persistent Scatterer Interferometry (PSI) exploits temporally stable scatterers to measure ground displacements with millimetric accuracy. However, the PSI technique faces inherent limitations when applied to complex structural systems such as long-span bridges. In these cases, the deformation may differ from the simple, linear models typically assumed for ground motion, leading to loss of phase coherence and phase ambiguity issues. Consequently, potentially valuable – but low-coherence – scatterers are often discarded from analysis, resulting in an incomplete interpretation of the structure behaviour. This work introduces a novel framework to integrate physics-based structural models (e.g. finite element models) with PSI to overcome these limitations. Rather than focusing on individual pixels, the model accounts for spatial correlation of the persistent scatterers using the structural model of the bridge. The method enables the inclusion of low-coherence Persistent Scatterers that would otherwise be excluded, enhancing the spatial density and reliability of displacement data. The methodology is applied to the Colle Isarco Viaduct (Vipiteno, Italy), a reinforced concrete bridge monitored with multi-temporal COSMO-SkyMed X-band SAR data. The infrastructure is also monitored with topographic survey measurements, which are used in this work as a validation benchmark to assess the accuracy of the results. Results demonstrate that the proposed framework successfully reduces uncertainty in LOS displacement for poorly coherent PSs from approximately 8 mm to 3 mm, within the uncertainty bounds of the benchmark. Furthermore, the enhanced interpretation of low-coherence points provides valuable insights into the bridge structural response and thermal deformation patterns.