High-resolution optical satellite data have become fundamental for acquiring global accurate remote sensing information (e.g., object geometric and spectral characteristics). However, due to the difficulty in obtaining accurate ground control points on a global scale, achieving accurate global positioning of satellite imagery remains a technical challenge. To realize global positioning optimization without relying on accurate control points, this paper leverages open-source data such as Google Earth orthophoto maps (GE maps) and FABDEM, and proposes the Coarse-to-Fine Open-Source Basemap Integration (CFBI) Method. The core idea of this method is to effectively eliminate gross errors in coarse control points by leveraging the differential projection offsets of roofs between single-view satellite images and multi-source orthophotos. On this basis, an iterative weight-selection adjustment strategy is adopted to achieve accurate positioning results. Experiments conducted in three regions, Jacksonville, New York, and Boston, demonstrate that the proposed algorithm significantly improves the positioning accuracy of satellite imagery, with an average enhancement of 62.92%, and accuracy in most areas reaching within 2 m.
Xu et al. (Sun,) studied this question.