Jul 2026· Electronics· Vol 15, pp. 2929· 0 citations
Abstract
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.
Landslides pose a persistent and catastrophic threat globally, necessitating advanced monitoring techniques for effective early warning and risk mitigation. Global Navigation Satellite System (GNSS) technology provides real-time, high-precision displacement data and is widely used in landslide monitoring. However, its sparse spatial sampling limits the ability to fully capture slope dynamics. To overcome this, the integration of GNSS with complementary sensing modalities has emerged as a critical research frontier. This review systematically examines the state-of-the-art in landslide displacement monitoring, focusing on the evolution from single-sensor GNSS applications to sophisticated multi-modal data integration methodologies. We first provide a comprehensive overview of various real-time GNSS positioning techniques (e.g., Real-Time Kinematic [RTK], Network Real-Time Kinematic [NRTK], and Precise Point Positioning-Real-Time Kinematic [PPP-RTK]) and critically compare their performance against other mainstream geodetic and geotechnical methods. Subsequently, we delve into four key integration paradigms: (1) The fusion of GNSS and accelerometers to capture the full spectrum of displacement from quasi-static creep to high-frequency motions; (2) The integration of GNSS and Interferometric Synthetic Aperture Radar (InSAR) to generate high-resolution, three-dimensional deformation fields with enhanced spatiotemporal coverage; (3) Data fusion of GNSS and other in-situ sensor, such as inclinometer to improve overall monitoring accurate; (4) The integration of surface and sub-surface monitoring data for more comprehensive description of landslide dynamics. Thereafter, the application of machine learning, evolving from data-driven predictive models to Physics-Informed Neural Networks (PINNs) are also reviewed, for intelligent displacement estimation and the interpretation of complex landslide trigger-response mechanisms. Finally, we discuss persistent challenges, including data quality, real-time processing, and system scalability, and propose future research directions centered on the development of landslide digital twins and intelligent, ubiquitous sensing networks. This review aims to provide a holistic reference for researchers and practitioners, charting a course toward more robust, intelligent, and predictive landslide monitoring systems.
Xuanyu Qu, Wenkun Yu, Xinrui Li et al.· Geohazards & Remediation· 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.
Abstract. Maritime vehicles face significant positioning challenges under adverse weather conditions where visual and laser SLAM systems suffer from severe degradation. Millimeter-wave radar offers inherent robustness to weather interference, yet single-band radar cannot simultaneously achieve accurate translation and robust attitude estimation.This paper proposes a complementary fusion framework for multi-band radar odometry.This system leverages W-band radar (CFEAR) for reliable translation estimation and combines it with X-band radar (LodeStar) to improve rotational estimation robustness. The main innovations are as follows:(1) A complementary fusion framework exploiting the complementary characteristics of W-band and X-band radar; (2) A quality-aware adaptive weighting mechanism dynamically computing fusion weights based on sensor data quality assessment; (3) A consistency gating mechanism monitoring inter-sensor agreement and activating protective measures during sensor degradation.Experiments on the MOANA maritime dataset demonstrate that the proposed method achieves stable and reliable local motion estimation, reaching an RTE RMSE of 1.67 m on the Near-Port sequence.
Fangcheng Qu, Yuheng Zhang, Xianlang Wei et al.· The International Archives o...· 0 citations
Continuous Global Navigation Satellite System (GNSS) monitoring is essential for characterizing surface subsidence in underground coal-mining areas. However, monitoring-station site selection remains strongly dependent on empirical judgment. Furthermore, steep deformation gradients can cause interferometric synthetic aperture radar (InSAR) decorrelation, phase-unwrapping failure, and data gaps in areas where ground-based monitoring is most needed. This study develops a quantitative, multi-source remote-sensing-assisted framework for GNSS monitoring-station site selection under a short-baseline real-time kinematic (RTK) configuration. Sentinel-1A, Sentinel-2C, unmanned aerial vehicle (UAV) photogrammetric products, and road-network data were integrated to construct eight evaluation factors: normalized difference vegetation index (NDVI), slope, terrain ruggedness index, deformation intensity, cumulative subsidence, InSAR coverage, distance to the subsidence edge, and distance to roads. An analytic hierarchy process (AHP) was used as the primary suitability assessment framework, while a random forest (RF) model was introduced with a limited weight to provide auxiliary information only in InSAR data-sparse areas. Grid-level aggregation, three-component Gaussian mixture model (GMM) screening, minimum-distance thinning, field reconnaissance, and monitoring-network review were subsequently combined to convert the continuous suitability surface into deployable station candidates. The resulting high-suitability areas were concentrated mainly along subsidence margins and near InSAR coverage gaps, reflecting the combined effects of deformation representativeness, supplementary monitoring demand, observation conditions, and engineering accessibility. Sixteen candidate stations were identified, of which S12, S3, and S10 were finally recommended to improve the northern, southwestern, and southeastern coverage of the existing monitoring network, respectively. Sensitivity analyses confirmed that the overall suitability pattern and recommended-station rankings remained stable under moderate parameter perturbations. Comparison with two existing GNSS stations further showed that the station with the higher suitability score exhibited a higher fitted subsidence rate (0.438 versus 0.320 mm day−1). Given the two-station and two-month validation dataset, this agreement represents preliminary consistency evidence rather than statistical proof of general effectiveness. The proposed framework links regional remote-sensing assessment with site-scale engineering review and monitoring-network optimization, providing practical decision support for GNSS deployment in underground mining-subsidence areas.
Yuanrong He, Xiao-Lin Yu, Huiwei Su et al.· Italian National Conference...· 0 citations
Structural health monitoring (SHM) of aging bridges requires reliable methods to capture deformation behavior at multiple scales. Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) provides millimeter-level displacement measurements, but interpretation of these datasets remains challenging for slender structures.
This study presents an anomaly detection framework for PS-InSAR displacement data combining temporal feature analysis and spatial structuring. Features such as velocity and thermal sensitivity are analyzed using an unsupervised Isolation Forest model, with SHapley Additive exPlanations (SHAP) used for interpretability. Persistent scatterers are projected onto the bridge axis and aggregated into span-scale zones, and a normalized relative anomaly density metric is introduced to enable cross-satellite comparison.
The framework is applied to descending-pass Sentinel-1 and RADARSAT Constellation Mission datasets over the Victoria Bridge in Montreal. Results show consistent identification of key anomalous segments across datasets despite differences in spatial resolution.
The proposed approach provides a structured and interpretable framework for PS-InSAR-based bridge monitoring.
Ehsan Sadeghian, D. Cusson, E. Dragomirescu et al.· e-Journal of Nondestructive...· 0 citations
Synthetic-aperture radar (SAR) ship detection is a fundamental task in maritime remote sensing, supporting wide-area surveillance, traffic monitoring, and emergency response under all-weather imaging conditions. Existing deep detectors mainly rely on spatial cues such as intensity, shape and context, but structured sea clutter and near-shore interference can still produce ship-like responses, while fine scattering details are weakened by deep downsampling. We address two practical representation limitations: incomplete preservation of shallow high-resolution details, and limited explicit modeling of local directional variation. To this end, we propose HMF-RTMDet, a shallow-neck spatial–frequency fusion detector. A P2 high-resolution path combines C2 features with upsampled P3 semantics. HybridMFBlock then processes the fused feature through a morphology branch and a trainable depthwise branch initialized by fractional Gabor templates, followed by channel-wise fusion. In the reported main HRSID run, HMF-RTMDet improves RTMDet-s from 67.9% to 72.6% in AP50:95, from 90.2% to 94.2% in AP50, and from 68.2% to 73.4% in APs. Across three runs, however, its AP50:95 is 72.17 ± 0.38%, comparable to the SFS-Conv and MCU-only controls. The evidence therefore identifies the P2 path as the main gain source but does not establish a stable advantage for HybridMFBlock over these controls. On SSDD, overall AP50:95 remains nearly unchanged and large-target performance decreases, defining an important boundary of the current design.
