On-orbit absolute radiometric calibration tracks sensor radiometric degradation and ensures accuracy for quantitative applications. Low-cost commercial satellites lack expensive on-board calibration systems, and traditional alternative methods cannot achieve automated, reliable calibration for large sensor fleets with low resource consumption. Pseudo-invariant calibration sites require no ground instruments, but commercial satellites’ heavy imaging schedules hinder frequent PICS observations. The Jilin-1 constellation is a large commercial constellation composed of satellites carrying multispectral imagers with resolution better than 1 m, and it has no on-board calibration system. Thus, an automated calibration method applicable to numerous imagers is needed, one that requires widely available clear-sky ground targets. Using MCD43A2 and MCD12Q1 products, we generate calibration region vectors to transfer the MODIS radiometric reference to Jilin-1. A look-up table (LUT) is constructed for the input parameters of the MODTRAN model. Calibration pixels and model input parameters are then extracted from Jilin-1 imagery using the region vectors, the LUT is interpolated to obtain the at-aperture radiance for each pixel and band, and on-orbit absolute radiometric calibration coefficients are calculated. The proposed method requires no ground-based synchronous experiments, achieves a high level of automation, and does not consume commercial imaging resources. The site calibration validation based on RadCalNet for the JL1GF02F PMS1 sensor shows that the maximum relative difference in the method across all bands is less than 4%.
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
Abstract. Matrix detectors and colour filters arrays are more widely used for satellites and rover missions in the past years. Recently, four CO3D (from “Constellation Optique 3D” in french) satellites equipped with COTS matrix Bayer sensor were launched and calibrated. Both the sensor sampling distinctive features and the new Step & Stare guidance mode are leading to new calibration and processing paradigms. In this paper, we delve into techniques dedicated for such Bayer matrix-based system, mainly but not limited to high-resolution (HR) Earth-observation (EO) satellite missions. We first describe dedicated techniques for in-orbit radiometric performance assessment like signal-to-noise ratio (SNR) and modulation transfer function (MTF). Then we address ground processing dedicated to Bayer acquisitions. Finally, we demonstrate the validity of our approach with CO3D in-orbit measurements. We also apply the radiometric ground processing on real images and provide a comparison with Pléiades-HR imagery, demonstrating the many benefits of the CO3D mission and all its novelties. CO3D in-orbit testing (IoT) is still ongoing eight months after launch, the in-flight performances are not presented in this paper due to confidentiality agreement.
Sylvain Lucas, Olivier Amram, C. Latry et al.· The International Archives o...· 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
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
Star cameras provide high-accuracy attitude determination for optical satellites, enabling precise geometric positioning of imagery without ground control points. However, affected by launch vibrations and thermal variations, the on-orbit geometric distortion differs significantly from ground measurement results, degrading attitude determination accuracy. Traditional methods typically rely on a physical model to characterize distortion, suffering from low accuracy at the detector edges. This article proposes a novel on-orbit geometric calibration method based on a block pointing model and angular distance consistency (BPMADC). Based on geometric distortion characteristics, the pointing calibration models for the main and local blocks are established. Using the consistency of stellar angular distances, the distortion parameters are solved without interference from attitude parameters. An order-staged solution and weight optimization are proposed to improve calibration accuracy. The calibration accuracy of star camera B of the Luojia3-02 satellite is improved from 0.352 pixels to 0.214 pixels. Furthermore, distortions at the detector edges are effectively calibrated. Validation data acquired on October 2, October 12, November 9, and November 26 are used to evaluate the calibration accuracy and stability of the camera parameters across different stellar regions and measurement times. The improvement rates are 48.249%, 26.856%, 44.049%, and 41.547%, respectively. Furthermore, experimental results demonstrate that the proposed method effectively improves geometric calibration accuracy and supports high-precision attitude determination for optical satellites.
Ning Zhang, Yanli Wang, Pin-Xi Liu et al.· IEEE Transactions on Geoscie...· 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
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