SEGPR: Semantic-Enhanced Geopositioning Refinement of Satellite Geographic Products by Combining Multitemporal Coarse-Accuracy Geographic Base Maps

High-resolution optical satellite geospatial products play a pivotal role in Earth observation science, as their geopositioning accuracy significantly impacts various geospatial applications. While conventional geometric correction methods rely on high-accuracy ground control points (GCPs), the global scarcity of such reference data has driven the development of GCP-free methodologies. Existing approaches, utilizing public base maps [e.g., Google Earth, shuttle radar topography mission (SRTM)] to generate coarse-accuracy GCPs (CAGCPs), predominantly employ single-temporal references, inherently constrained by the base maps’ intrinsic positioning errors. Although recent attempts to manually extract multitemporal control points have partially reduced random planar errors, these methods critically neglect substantial systematic and gross errors in both elevation and planar dimensions, particularly from off-ground features in urban areas. To further improve geopositioning accuracy using multitemporal base maps, this article introduces a novel method of semantic-enhanced geopositioning refinement (SEGPR), which refines the available CAGCPs through the incorporation of semantic information and error adjustment techniques with multitemporal base maps. Specifically, this article employs a robust method to automatically capture coarse-accuracy matching control points (CAMCPs) using deep features from multitemporal base maps. Subsequently, this article innovatively explores the error characteristics of CAMCPs on both ground and off-ground features. Based on this exploration, semantic-guided random sample consensus (SGRANSAC) is introduced as a coarse correction to address elevation systematic errors, planar projection errors, and gross errors in off-ground MCPs, ensuring reliable MCP selection. In addition, the proposed elevation-augmented weight iteration (EAWI) iteratively reduces the influence of systematic elevation and random planar errors related to ground MCPs, enhancing GCP accuracy, particularly in elevation. The proposed algorithm is evaluated on geometric positioning and modeling accuracy. Experimental results across five global regions demonstrate an average geometric positioning accuracy of 1.42 m, a 55.90% improvement over state-of-the-art nonbasemap methods, and a 50.52% enhancement compared to state-of-the-art basemap methods. All corrected products achieve absolute geometric positioning accuracies better than 2 m, and modeling accuracy improves by 18.37%. The implementation of the proposed algorithm is available at https://github.com/graduate-2024/SEGPR