지상기준점 패치 기반 드론 영상 자동 지오레퍼런싱

Ground control points (GCPs) are essential for the precise georeferencing of drone imagery, but traditional manual measurement methods are inefficient and resource-intensive. Research on automated georeferencing methods utilizing GCP patches is necessary to address this issue. The purpose of this study is to propose and validate an automated method that improves georeferencing accuracy for drone imagery by leveraging GCP patches. The proposed method combines iterative automatic measurement and bundle adjustment to improve measurement accuracy incrementally. A Random Forest-based binary classifier was employed to incorporate only reliable measurement results, enhancing the overall georeferencing accuracy. The method’s performance was validated using datasets with significant temporal gaps, and iterative refinement was performed to progressively adjust the matching range, exterior orientation parameters, and interior orientation parameters. Experimental results show that the Random Forest classifier achieved a high Area Under the ROC Curve (AUC) of 0.9, and the final positional accuracy, based on checkpoints, reached 6 cm. Additionally, the success rate and precision of automatic measurements improved significantly through the iterative process.