Jul 2026· The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences· 0 citations· 2 references
Abstract
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.
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
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.
A. Johnsy, Eyrin Kim, Qiaoping Zhang et al.· The International Archives o...· 0 citations
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
On-orbit relative radiometric calibration (RRC) is a fundamental prerequisite for quantitative remote sensing analysis and high-level product generation. Although side-slither maneuvers provide a robust means for calibration, existing methodologies are often constrained by regularization inaccuracies, uneven distribution of gray-scale samples, and limited adaptability to complex multisensor architectures. This article proposes a unified RRC framework to overcome these challenges. The process begins with a prior-guided optimization method for data regularization, which operates independently of linear features or edges. To handle the inherently uneven distribution of gray-scale samples across natural scenes, an adaptive clustering-based method is implemented to estimate calibration coefficients. This approach ensures stable performance across the observed effective dynamic range, particularly in gray-scale ranges where samples are sparse. Furthermore, the framework incorporates a detail-aware strategy to achieve high-precision calibration across the full field of view (FOV). Specifically, the virtual steady reimaging (VSRI) model is first leveraged to achieve rigorous spatial alignment of identical ground features across multiple sensors. Based on this precise geometric alignment, the radiometric calibration is subsequently anchored to an optimal reference radiometric state. This approach effectively eliminates cross-chip inconsistencies while preserving structural details. Validation using side-slither and push-broom data from the Intelligent Remote Sensing Satellite-1 (IRSS-1), Luojia3-02 (LJ3-02), and Ziyuan-1F (ZY-1F) satellites demonstrates the effectiveness of our proposed approach across diverse scenes, sensor architectures, and spectral bands. Comparative analyses show that the proposed method achieves overall superior performance over four state-of-the-art methods in removing striping artifacts and maintaining radiometric fidelity.
Tao Peng, Ru Chen, Qianyu Wu et al.· IEEE Transactions on Geoscie...· 0 citations
In this paper, we evaluated image quality, algorithms and a workflow associated with matching horizons derived from 3D terrain data to ground imagery as a way to visually estimate a geographic position. The evaluation represented a passive, vision-based navigation technique using long-standoff (>1 KM) terrain features and a horizon detection algorithm hosted within an Android-based geospatial application. Our method involved quantitatively grading edge image feature quality based on pixel data between sky and terrain from high to poor. We tested the algorithm and processing to derive a position using images of varied quality, representing fine and gross, and near and far geographic terrain structures. The site chosen for our tests was located near the Organ Mountains in New Mexico to take advantage of largely unobstructed, long-distance features that challenged both image quality and horizon detection. Testing used the Samsung S23 Ultra (S23U) phone’s primary internal camera to acquire the necessary ground images and native compute power. Our evaluation workflow featured both pre-processing and near-real-time processing elements for position estimations. Pre-processing involved building a Geopackage containing geolocated, synthetic horizons extracted from available 3D terrain data of the test area and camera/sensor configuration data. These data were pre-loaded onto the phone to accomplish the live, near-real-time positional determinations matched to the extracted horizons generated from images acquired by the S23U camera. Our results showed that single-image processing, where only one high-quality ground photo was acquired, 75% of solutions were within 100 m of the actual camera position (compared with the internal sensor-based, Exchangeable Image File Format (EXIF) metadata). Single images of fair quality resulted in positional accuracies where only 38% of the solutions were within 100 m of the EXIF. Improvement was realized when four or more images from varying directions collected from a single location resulted in over 90% of positions falling within 100 m of the EXIF.
J. Ruby, Jimmy R. Carter, Melissa Pham et al.· Applied Sciences· 0 citations
Abstract. The measurement of Ground Control Points is a crucial but time-consuming step in photogrammetric surveys, especially in the Built Heritage domain. This study investigates the use of a new generation Global Navigation Satellite System receiver equipped with an integrated camera to measure inaccessible points (to be used as GCPs, e.g. on building façades), combining satellite positioning and photogrammetry in a single device. The approach was tested on a historical building using both the tested GNSS-camera system and traditional Total Station topographic measurements as reference. Results show that the proposed method can achieve centimetric accuracy, with horizontal and vertical errors of about 3-5 cm (in line with the adopted NRTK service). While a small systematic shift was observed, the overall geometric consistency of the measured points remained within acceptable limits for many heritage documentation applications. Additionally, the georeferenced images acquired by the system were successfully used in a Structure from Motion (SfM) direct georeferencing workflow, producing 3D models and orthophotos with good metric reliability when compared to other data, such as TLS scans. The study demonstrates that GNSS-camera systems can significantly reduce fieldwork time while providing reliable spatial data, representing a promising solution to be integrated into heritage documentation pipelines in an efficient and flexible way.
L. Teppati Losè, F. Chiabrando, F. Giulio Tonolo· The International Archives o...· 0 citations
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