Robust Hierarchical Point Matching Between Aerial and Ground Imagery Through Depth Map‐Based Partitioned Attention Aggregation

ABSTRACT Reliable feature point matching is a pivotal step in the integrated 3D reconstruction of aerial and ground images. Nonetheless, the substantial viewpoint and scale differences between aerial and ground images introduce significant challenges in feature point matching. To address this issue, this study presents a robust matching method for feature points from these two types of images, employing partitioned attention aggregation along with a coarse‐to‐fine hierarchical matching approach, enabling efficient matching of large‐scale aerial‐ground images even with hardware limitations. In the coarse matching phase, region segmentation is carried out on the ground image based on its depth map, which is computed using a monocular depth estimation network. Subsequently, a graph neural network serves as an intermediary for information exchange between features, and partitioned attention aggregation is performed while adhering to the constraints imposed by the segmented image regions. During this process, feature points are grouped to form distinct subgraphs, enhancing the structural similarity between corresponding graphs when there are significant depth and viewpoint changes. This also reduces redundant connections within graph neural networks, leading to efficient feature matching. Corresponding local patches are generated from the full‐sized aerial‐ground images using the initial matches, and further feature point extraction and matching from these patches yield accurate point matches. The experimental results indicate the proposed method performs better than state‐of‐the‐art matching methods. Specifically, compared with the best‐performing method among the comparative ones, our method enhances the average number of correct matches and the average matching precision by approximately 6‐fold and 36%, respectively.