A Unified On-Orbit Relative Radiometric Calibration Framework for Optical Remote Sensing Satellites Based on Side-Slither Observations
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.