Jul 2026· The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences· 0 citations· 5 references
Abstract
Abstract. High revisit-rate Synthetic Aperture Radar (SAR) constellations generate large volumes of imagery that require consistent geolocation accuracy to support applications such as change detection and interferometry. However, variations in orbit determination, attitude knowledge, and external factors such as Global Navigation Satellite System (GNSS) interference can introduce geolocation errors that vary across acquisitions, making large-scale validation challenging. This study presents an automated approach to detect and quantify geolocation offsets in ICEYE SAR imagery by aligning orthorectified scenes with reference images using feature-based matching and correlation-based refinement. The method is validated against independently derived absolute geolocation measurements from corner reflector calibration sites in the United States, Canada, Australia, and Poland. Evaluation across 726 acquisitions demonstrates strong agreement with reference measurements, achieving an overall root-mean-square error (RMSE) of 1.39 m, with RMSE values of 1.18 m for Spotlight mode and 1.93 m for Stripmap mode. Operational applicability is demonstrated through large-scale acquisition campaigns, including nationwide Stripmap coverage over Japan and coherent image stack analysis. The results show that the proposed method can reliably estimate geolocation offsets, detect anomalies, and monitor geometric consistency across large SAR archives, providing a practical and scalable solution for automated geolocation quality control in micro-satellite SAR constellations.
The rapid advancement of synthetic aperture radar (SAR) satellites has enabled enhanced capabilities for all-weather and all-time Earth observation. Nevertheless, the geolocation accuracy of SAR images remains constrained by multiple error sources. Although ground calibration fields can substantially improve the geometric geolocation accuracy of SAR images, such procedures are typically time-consuming and labor-intensive. To address this issue, this article proposes an SAR terrain self-calibration method based on the projected area ratio (PAR), aiming to improve the geometric positioning accuracy of SAR ortho-images cost-effectively and efficiently. First, a novel concept, referred to as the projected area ratio, is introduced to quantitatively assess and compare the geometric distortions among different SAR images. This metric has a clear physical interpretation and enables effective discrimination between steep and flat terrain within SAR imaging regions. Subsequently, we propose a constrained generalized search-based clustering algorithm to efficiently extract tie points. The high correspondence between PAR maps and SAR images is leveraged to estimate SAR geometric error compensation. Finally, the proposed method is validated using four SAR images acquired over different terrain conditions, demonstrating that PAR can serve as a quantitative indicator of SAR geometric distortion. The applicability of the proposed method is further discussed based on the physical interpretation of PAR. Compared with the conventional GAMMA method, the proposed method demonstrates significant advantages in the matching success rate. In addition, corner reflectors are employed to quantitatively evaluate the geolocation accuracy of the corrected SAR orthorectified images. The experimental results show that the positioning accuracy is improved from approximately 80 to 3.952 m after applying the proposed method, demonstrating its effectiveness in enhancing SAR geolocation accuracy and enabling high-precision positioning for low-accuracy SAR satellites.
Haisong Weng, Zhiming Liu, Qiming Yuan et al.· IEEE Transactions on Geoscie...· 0 citations
Abstract. This study presents a self-calibration method for optical Earth observation satellites equipped with matrix sensors. Precise geolocation of each pixel in a satellite image requires accurate modelling of the acquisition geometry, typically achieved through refinement that corrects the geometry using measurements such as correspondences between image pixels and ground coordinates, or between pixels in different images. A critical aspect of this modelling involves the sensor's internal geometry, which defines the line-of-sight (LOS) vector for each pixel in the focal plane. The calibration method proposed in this article eliminates the need for exogenous data (e.g., higher-resolution satellite images or airborne sensor imagery) by relying solely on a set of acquisitions in a specific configuration. The method is evaluated and validated using CO3D imagery. Several evaluation criteria were developed for this purpose, including the reliability of the refinement process, the quality of tie-point intersections, inter-site result comparisons, and alignment with absolute references. This paper does not include the in-orbit performances due to confidentiality agreement.
Guillaume Laurent, Alice Latourte, Fabrice Buffe et al.· The International Archives o...· 0 citations
Precise assessment of satellite altimeter performance is essential to understand and monitor global sea level rise with confidence. This investigation introduces a new approach based on at least two co-located point targets called differential range calibration (DiRaC), which monitors the performance of satellite altimeters. By analyzing differential biases between point targets, DiRaC mitigates common errors (e.g., those related to atmospheric effects, geophysical corrections, and orbit inaccuracies) that affect absolute range calibration. The methodology is demonstrated using two rectangular corner reflectors (CRs) deployed at a dedicated calibration site (called ALX) in Crete, Greece, under the framework of European Space Agency’s (ESA) Permanent Facility for Altimetry Calibration (PFAC). Fully focused synthetic aperture radar (FF-SAR) processing is applied to isolate the radar returns of each reflector, enabling both absolute range bias estimation for Sentinel-6 Michael Freilich (Sentinel-6 MF) and the application of DiRaC. An initial assessment revealed a variability of 6 mm in differential range bias, which, given the current sample size, corresponds to an estimated standard error of 1 mm. This variability is attributed to the combined effect of the intrinsic altimeter precision and a processing-related correlated component. A quantitative assessment of the individual contribution of these two effects requires an accurate characterization of the processing-related correlated component. Hence, with an additional correlation analysis, the standard error of 1 mm can be more accurately attributed to the Sentinel-6 MF altimeter performance.
Costas Kokolakis, D. Piretzidis, S. Mertikas et al.· IEEE Transactions on Geoscie...· 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
Routine Synthetic Aperture Radar (SAR) acquisitions from the Sentinel‐1 constellation enable monitoring of slow surface deformation (millimeters to centimeters per year), yet consistent and accessible pathways for producing validated, user‐ready velocity products remain limited. Here we present (a) a statewide Interferometric SAR (InSAR) line‐of‐sight secular surface velocity map for California derived from standardized Advanced Rapid Imaging and Analysis Sentinel‐1 Geocoded Unwrapped Phase (ARIA‐S1‐GUNW) products spanning 2014–2023, and (b) the details of a scalable processing and validation workflow for producing similar maps. Our workflow leverages several open‐source software tools for post‐processing of GUNW products, time series analysis, corrections for noise, and removal of earthquakes and transient signals. We apply it to data from nine ascending and descending Sentinel‐1 tracks across California, and validate the output against Global Navigation Satellite Systems (GNSS) data. The resulting velocity fields resolve tectonic and anthropogenic deformation signals, including elastic strain accumulation and shallow creep along the San Andreas Fault system, rapid subsidence exceeding 10 cm/yr in the Central Valley, and localized deformation associated with groundwater withdrawal and landsliding. We find millimeter‐per‐year agreement with GNSS velocities, and estimate typical velocity uncertainties to be ≤1 mm/yr over coherent terrain, increasing locally in regions of strong deformation gradients and/or decorrelation. Our velocity map provides an accessible data set for non‐specialists to investigate multiple sources of slow secular deformation across California; our workflow can also be used with GUNW products from the NASA‐ISRO SAR (NISAR) mission, enabling the leveraging of that rich data source in the future.
S. Sangha, G. Funning, M. Govorcin et al.· Earth and Space Science· 0 citations
Abstract. As many observation data providers are delivering spaceborne optical images with a resolution close to airborne sources, users are expecting the same level of geolocation accuracy. Unfortunately, locating the line of sight of a camera on the ground from, at least, 500km is still a challenge. It requires a precise knowledge of the camera absolute orientation beyond current technology. Spaceborne SAR images do not suffer from this problem as the location accuracy depends only on the antenna position and speed. An operational approach is presented exploiting the geometric stability of SAR sensors to improve the location accuracy of optical images. It is based on the ability to find correspondences between overlapping SAR and optical images. The refinement of camera orientations is then achieved through photogrammetric (almost) classical techniques. A medium scale validation campaign conducted across France demonstrated an absolute horizontal geolocation accuracy of 1.5m (CE90).
A. Barot, P. Favé, L. Gabet et al.· The International Archives o...· 0 citations
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