Beyond Tie Points: Satellite Image Block Adjustment based on Dense Feature Consistency

Abstract

Owing to the weak stereo geometry of satellite images, Planar Block Adjustment (PBA) is a predominant technique for correcting geometric distortions in satellite images, which treats elevation as a known constraint and primarily optimizes planar coordinates. Existing PBA methods mainly rely on explicit tie points, suffering from parallax caused by inaccurate elevation (e.g., near high buildings) and irreversible error accumulation, which severely degrades adjustment accuracy. In this paper, a "Beyond Tie Points" paradigm for satellite image adjustment is proposed. A pretrained feature extractor is employed to extract robust dense features and a parallax-aware confidence map from each image. A gridded coarse-to-fine optimization framework then directly solves for the adjustment parameters basing on confidence-weighted feature consistency. Experiments conducted on multiview satellite image datasets covering Beijing, Guangzhou and San Jose demonstrate that the proposed method is significantly superior to traditional approaches in both accuracy and robustness, reducing the average error by up to 75.43% compared to traditional PBA.