Abstract: To address the limitations of geometric accuracy in high-resolution satellite image production,which relies on orthophoto control sources and suffers from low efficiency due to manual ground control point selection,an efficient and automated geometric accuracy improvement method is urgently needed.This paper proposes an automated geometric control framework based on control image blocks,which integrates adaptive image segmentation and epipolar geometric constraints.An automatic control point matching and hierarchical bundle adjustment workflow is developed to robustly consolidate multi-source control information and enhance geometric constraints.Experiments demonstrate that the proposed method achieves control point matching accuracy comparable to manual operations across multiple typical regions,with matching errors consistently maintained within a reasonable range and stable performance across cross-resolution scenarios.The method offers strong scalability of control data and significantly improves the automation efficiency and geometric accuracy of high-resolution satellite image production,showing promising potential for engineering applications.

Key words: control image, adaptive segmentation, designated point matching, hierarchical control adjustment