A Geometry-Constrained Framework for Automatic Geometric Positioning Accuracy Assessment of Large-Scale Satellite Imagery

Abstract

High-resolution optical satellite constellations continuously generate massive volumes of remote sensing imagery, making automatic, ground-control-point-free (GCP-free) geometric positioning accuracy assessment increasingly important for ensuring the quality of downstream applications. However, conventional GCP-free inspection methods based on local feature matching often exhibit limited robustness under large initial positioning errors, weak-texture regions, cloud contamination, and temporal appearance variations, resulting in poor generalization across large-scale production scenarios. To address these challenges, this paper proposes a geometry-constrained framework that integrates Rational Polynomial Coefficient (RPC) prior constraints, coarse-to-fine registration, adaptive match-density-based block selection, hierarchical geometric verification, and a geolocation residual confidence measure into a unified automatic quality inspection pipeline. The framework leverages LoFTR for dense feature matching, but its principal contribution lies in the system-level integration and operational design for large-scale industrial satellite image production. Extensive experiments on multi-satellite and multi-scene datasets from the Jilin-1 satellite series show that the proposed method achieves a median positioning error below 2 m, an Average Precision (AP) improvement of 0.53 over the baseline, and nearly perfect accuracy on the evaluated test set for confidence thresholds above 0.5. The framework has also been deployed in the operational production system of multiple commercial Jilin-1 missions for more than six months, demonstrating its effectiveness, robustness, scalability, and practical applicability for large-scale optical satellite imagery.