SemGeoFrame: A Visual Matching Framework for Aircraft Based on Surface Semantic Information

In GNSS-denied environments, UAV visual positioning faces the critical bottleneck of low matching accuracy between heterogeneous images. To address this, we propose SemGeoFrame, a visual matching framework that leverages surface semantic information to enhance robustness. The key innovations are threefold: First, we construct a semantic prior from the probability distributions of image semantic segmentation and design a consistency screening mechanism based on Jensen–Shannon divergence to eliminate false matches by leveraging pixel-level semantic consistency for cross-view image matching. Second, a confidence-guided partition sampling strategy ensures balanced distribution of matches in both spatial and semantic categories, overcoming the limitations of conventional spatial-only sampling. Third, geometric, semantic, and confidence constraints are jointly optimized to achieve robust homography estimation. SemGeoFrame adopts a plug-and-play design and consistently improves the performance of mainstream matching algorithms (e.g., ORB, SuperPoint, LoFTR) on multiple heterogeneous datasets. The experimental results demonstrate that our framework significantly enhances matching accuracy and robustness across diverse scenarios.