Aug 2026· e-Journal of Nondestructive Testing· Vol 31· 0 citations
Abstract
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.
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.
Frida Carlvik, S. Aminjafari, L. Eriksson et al.· e-Journal of Nondestructive...· 0 citations
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.
Roberto Monti, F. Gaspari, Rohollah Naeijian et al.· The International Archives o...· 0 citations
Global Navigation Satellite System Reflectometry Interferometric Synthetic Aperture Radar (GNSS-R InSAR) offers all-weather, all-day observation capabilities and high temporal resolution, enabling elevation deformation monitoring with a single satellite. However, in hazardous regions, such as tailings dam slopes, measuring the deformation of a greater number of target points is essential for a more accurate assessment of geological hazard risks. Since navigation satellite signals are not originally designed for imaging purposes, their inherent narrow bandwidths result in low spatial resolution and limited target recognition capabilities, rendering them inadequate for such scenarios. To address these limitations, this paper investigates a GNSS-R InSAR deformation measurement architecture utilizing dual-frequency BeiDou-3 (BDS-3) signal fusion. Specifically, a coherent spectrum fusion method is introduced to effectively expand the signal bandwidth, thereby significantly enhancing range resolution and target identification capabilities. Building upon this, deformation measurements are conducted to achieve more refined and detailed monitoring.
Abstract. Satellite-based bridge monitoring with Interferometric Synthetic Aperture Radar (InSAR) offers a cost-effective solution for continuous deformation analysis of critical transport infrastructure. For very high resolution VHR SAR data, distributed scatterers DS suitability for bridge monitoring remains uncertain because elevated decks may suffer layover and partial pixel mixing with underlying terrain. This study evaluates the applicability of DS for bridge monitoring using 23 TerraSAR-X Staring Spotlight over two bridges near Regensburg, Germany: a 930 m long bridge crossing the Danube and agricultural land, and a low overpass crossing the A3 motorway. Persistent Scatterer Interferometry (PSI) was performed with a combined PS/DS workflow, and residual terrain error (RTE) estimates were validated against airborne laser scanning data. The results show that DS can increase measurement density compared with pure persistent scatterers (PS), but their reliability strongly depends on the radiometric contrast between the bridge and its surroundings. For the large bridge over heterogeneous terrain, DS and PS on structural elements yielded physically plausible RTE estimates. In contrast, for the overpass above asphalt surfaces with similar backscatter characteristics, DS were frequently affected by layover between bridge deck and ground, producing large vertical biases, with mean residuals reaching -6.09 m on the carriageway and remaining at -2.12 m even after filtering to above-ground points. The results demonstrate that DS-based bridge monitoring in VHR SAR is feasible where surrounding surfaces are radiometrically distinct; otherwise, DS may lead to erroneous RTE estimation and deformation interpretation.
Stefan Scheiblauer, Francescopaolo Sica, Michael Schmitt· The International Archives o...· 0 citations
Major earthquakes pose critical risks to urban infrastructure and human safety, necessitating rapid, precise, and quantitative post-event damage assessments. This study introduces a novel comparative framework for evaluating infrastructure damage from the 2024 Noto earthquake in Japan and the 2019 Mianeh earthquake in Iran using multi-temporal radar datasets: Sentinel-1 (C-band) and ALOS PALSAR-2 (L-band). Data preprocessing—including geocoding, radiometric calibration, and speckle filtering—was conducted to produce high-resolution coherence and backscatter intensity maps. Sequential coherence analysis identified reductions of 0.25–0.45 in severely impacted urban sectors, with the most pronounced declines in dense residential areas. Complementary backscatter intensity changes confirmed building collapses and surface deformations, particularly in industrial and critical infrastructure zones. Our results reveal that Sentinel-1 excels in capturing superficial, short-wavelength surface disruptions, while ALOS PALSAR-2 effectively detects deeper structural deformations. Importantly, integrating both datasets enhanced spatial accuracy of damage detection by approximately 18%, demonstrating a robust, quantitative methodology for rapid post-earthquake damage assessment. This integrated SAR approach offers a powerful tool for informed urban resilience planning, prioritizing reconstruction efforts, and advancing disaster response strategies.
Fatemeh Sanaei, S. Karimzadeh, B. Feizizadeh· Turkish Journal of Remote Se...· 0 citations
(English) Interferometric Synthetic Aperture Radar (InSAR) enables millimeter-level measurements of surface deformation over large areas and long time spans, and has become an important tool for geohazard monitoring and infrastructure safety assessment. However, in natural environments with dense vegetation, intensive agricultural activity, or strong surface disturbance, rapid variations in scattering mechanisms often cause severe coherence loss, which significantly limits the accuracy and density of deformation monitoring. By introducing multi-polarization observations, multi-temporal Polarimetric InSAR (MT-PolInSAR) improves InSAR performance in complex low-coherence scenarios. Nevertheless, existing MT-PolInSAR methods still face two major limitations: insufficient consideration of the spatial and temporal variability of scattering mechanisms, and inadequate exploitation of the complementary information in the polarimetric, temporal, and spatial domains within a unified framework.
To address these issues, this thesis investigates phase optimization and deformation monitoring for MT-PolInSAR under low-coherence conditions. A systematic methodology is developed by exploiting the redundancy and scattering information contained in polarimetric SAR data, including homogeneous filtering for small datasets, polarimetric phase optimization with spatially varying scattering mechanisms, joint phase optimization in the temporal and polarimetric domains, and sequential near-real-time processing.
First, a homogeneous filtering method for MT-PolInSAR small datasets is proposed. By introducing spatial covariance structures and jointly exploiting temporal and polarimetric redundancy, the method improves pixel discrimination and enhances the signal-to-noise ratio. Experiments on simulated data and Barcelona Airport data demonstrate improved phase quality, more stable coherence estimation, and better preservation of spatial structures.
Second, an improved polarimetric phase optimization method, termed ImESPO, is proposed to account for spatial variations in scattering mechanisms. Unlike conventional methods, it explicitly considers local scattering heterogeneity during polarimetric projection. Results show that ImESPO achieves more stable coherence gains and phase consistency in heterogeneous areas, improving phase estimation accuracy by more than 20%.
Third, a joint phase optimization model combining the temporal and polarimetric dimensions, termed JPTPO, is developed. By jointly modeling both dimensions within a unified statistical framework, the method achieves improved phase consistency and more stable deformation inversion results on both simulated and real datasets.
Finally, a near-real-time MT-PolInSAR deformation monitoring method is proposed for rapid-decorrelation scenarios. Applied to landslide monitoring in the Fengjie area of the Three Gorges Reservoir, the proposed method increases measurement density by a factor of four and improves monitoring accuracy from 18.4% to 71.8%, while maintaining near-real-time capability.
Overall, this thesis advances the theory and methodology of MT-PolInSAR deformation monitoring in complex low-coherence environments, providing new solutions for high-precision and continuous monitoring of landslides and other geohazards.
(Català) El radar d’obertura sintètica interferomètric (InSAR) permet mesurar deformacions superficials amb precisió mil·limètrica en grans àrees i al llarg de períodes prolongats, i s’ha convertit en una eina clau per a la monitorització de riscos geològics i l’avaluació de la seguretat d’infraestructures. Tanmateix, en entorns naturals amb vegetació densa, activitat agrícola intensa o fortes pertorbacions superficials, les variacions ràpides dels mecanismes de dispersió solen provocar una pèrdua severa de coherència, fet que limita significativament la precisió i la densitat de la monitorització de deformacions. Gràcies al desenvolupament del SAR polarimètric (PolSAR), l’InSAR polarimètric multitemporal (MT-PolInSAR) incorpora observacions multipolaritzades i millora el rendiment de l’InSAR en escenaris complexos de baixa coherència. No obstant això, els mètodes MT-PolInSAR existents encara presenten dues limitacions principals: consideren de manera insuficient la variabilitat espacial i temporal dels mecanismes de dispersió, i no aprofiten plenament la informació complementària dels dominis polarimètric, temporal i espacial dins d’un marc unificat.
Per abordar aquests problemes, aquesta tesi estudia l’optimització de fase i la monitorització de la deformació mitjançant MT-PolInSAR en condicions de baixa coherència. Aprofitant la redundància i la informació de dispersió contingudes en les dades SAR polarimètriques, es desenvolupa una metodologia sistemàtica que inclou el filtratge homogeni per a conjunts de dades petits, l’optimització polarimètrica de fase amb mecanismes de dispersió espacialment variables, l’optimització conjunta en els dominis temporal i polarimètric, i el processament seqüencial gairebé en temps real.
En primer lloc, es proposa un mètode de filtratge homogeni per a conjunts petits de MT-PolInSAR. Mitjançant la introducció d’estructures de covariància espacial i l’explotació conjunta de la redundància temporal i polarimètrica, el mètode millora la discriminació de píxels i augmenta la relació senyal-soroll. Els experiments amb dades simulades i amb dades de l’Aeroport de Barcelona mostren una millor qualitat de fase, una estimació de coherència més estable i una millor preservació de les estructures espacials.
En segon lloc, es proposa un mètode millorat d’optimització de fase polarimètrica, anomenat ImESPO, per considerar la variació espacial dels mecanismes de dispersió. A diferència dels mètodes convencionals, el mètode proposat incorpora explícitament l’heterogeneïtat local durant la projecció polarimètrica. Els resultats mostren que ImESPO aconsegueix guanys de coherència més estables i una millor consistència de fase en àrees heterogènies, amb una millora superior al 20 % en la precisió de l’estimació de fase.
En tercer lloc, es desenvolupa un model d’optimització conjunta de fase que combina les dimensions temporal i polarimètrica, anomenat JPTPO. En modelar ambdues dimensions dins d’un marc estadístic unificat, el mètode millora la consistència de fase i l’estabilitat de la inversió de deformació en dades simulades i reals.
Finalment, es proposa un mètode de monitorització de deformacions MT-PolInSAR gairebé en temps real per a escenaris de decorrelació ràpida. Aplicat al seguiment d’esllavissades a la zona de Fengjie, a l’embassament de les Tres Gorges, el mètode incrementa la densitat de mesura en un factor de quatre i millora la precisió de monitorització del 18,4 % al 71,8 %, mantenint la capacitat de processament gairebé en temps real.
En conjunt, aquesta tesi amplia la teoria i la metodologia de MT-PolInSAR per a la monitorització de deformacions en entorns complexos de baixa coherència, i proporciona noves solucions per a la monitorització contínua i d’alta precisió d’esllavissades i altres riscos geològics.
(Español) La interferometría radar de apertura sintética (InSAR) permite medir deformaciones superficiales con precisión milimétrica en grandes áreas y durante largos periodos, por lo que se ha convertido en una herramienta clave para la monitorización de riesgos geológicos y la evaluación de la seguridad de infraestructuras. Sin embargo, en entornos naturales con vegetación densa, actividad agrícola intensa o fuertes perturbaciones superficiales, las rápidas variaciones de los mecanismos de dispersión suelen provocar una pérdida severa de coherencia, lo que limita significativamente la precisión y la densidad de la monitorización de deformaciones. Gracias al desarrollo del radar polarimétrico de apertura sintética (PolSAR), el InSAR polarimétrico multitemporal (MT-PolInSAR) incorpora observaciones multipolarizadas y mejora el rendimiento del InSAR en escenarios complejos de baja coherencia. No obstante, los métodos MT-PolInSAR existentes aún presentan dos limitaciones principales: consideran de forma insuficiente la variabilidad espacial y temporal de los mecanismos de dispersión, y no aprovechan plenamente la información complementaria de los dominios polarimétrico, temporal y espacial dentro de un marco unificado.
Para abordar estos problemas, esta tesis estudia la optimización de fase y la monitorización de deformación mediante MT-PolInSAR en condiciones de baja coherencia. Aprovechando la redundancia y la información de dispersión contenidas en los datos SAR polarimétricos, se desarrolla una metodología sistemática que incluye filtrado homogéneo para conjuntos de datos pequeños, optimización polarimétrica de fase con mecanismos de dispersión espacialmente variables, optimización conjunta en los dominios temporal y polarimétrico, y procesamiento secuencial casi en tiempo real.
En primer lugar, se propone un método de filtrado homogéneo para conjuntos pequeños de MT-PolInSAR. Mediante la introducción de estructuras de covarianza espacial y el aprovechamiento conjunto de la redundancia temporal y polarimétrica, el método mejora la discriminación de píxeles y aumenta la relación señal-ruido. Los experimentos con datos simulados y con datos del aeropuerto de Barcelona muestran una mejor calidad de fase, una estimación de coherencia más estable y una mejor preservación de las estructuras espaciales.
En segundo lugar, se propone un método mejorado de optimización de fase polarimétrica, denominado ImESPO, para considerar la variación espacial de los mecanismos de dispersión. A diferencia de los métodos convencionales, el método propuesto incorpora explícitamente la heterogeneidad local durante la proyección polarimétrica. Los resultados muestran que ImESPO logra ganancias de coherencia más estables y una mejor consistencia de fase en áreas heterogéneas, con una mejora superior al 20 % en la precisión de estimación de fase.