Quality Assessment and Credibility Generation Method for Ground-Based Photometric Data of GEO Objects

Ground-based photometric data of high-orbit targets are essential for attitude estimation and physical characterization, but their quality is degraded by atmospheric turbulence, sky background, and tracking instabilities. Existing methods rely on discrete binary flags or statistical outlier rejection, lacking fine-grained credibility quantification and failing to exploit the physical prior of light curve smoothness. This paper proposes a quality assessment framework that replaces binary labels with continuous credibility scores. The method first segments data into continuous arcs, removes transient edge points, and extracts smooth trends. Then, three credibility factors (elevation angle, arc duration, and local residual robustness) are combined with hard rejection rules to yield a comprehensive score between 0 and 1. Daily and target-level scores are further synthesized for multi-granularity quality comparison. Experiments on real high-orbit photometric data show that the hard rejection ratio is below 3%, credibility distributions align well with observed data quality, and daily scores effectively distinguish good from poor observation nights. The generated credibility can be directly used as sample weights in downstream models, enhancing the reliability of optical data analysis.