Robust Image Registration via Consistent Topology Sort and Vision Inspection

Machine vision plays a crucial role in Earth observation. As a fundamental and challenging task in vision systems, image registration faces new challenges due to increasing collaborative and customization applications. The prevalence of more false matches and low-precision matches is particularly evident in complex and changeable scenarios. In this article, we propose a robust image registration method via topology sort and vision consistence. Initial candidate matches are established via the nearest neighbor ratio of image intensity descriptors. A topological sort across the proximity structure around the point pairs is defined to assess the reliability of candidate matched pairs, effectively eliminating more false matches while retaining highly reliable point pairs. To preserve more point pairs, we develop a spatial visual inspection mechanism to further determine the potential matches from the remaining pairs that do not satisfy the previous topological constraint. During vision inspection, the spatial transformation model is simultaneously estimated. Experimental results on public datasets show that the proposed method outperforms state-of-the-art approaches in both matching accuracy and visual effect.