Efficient match pair selection for matching large-scale oblique UAV images using spatial priors

Abstract

ABSTRACT Image matching is critical for photorealistic 3D reconstruction based on oblique images. An efficient match pair selection methodology for matching large-scale oblique UAV images is proposed in this paper. The proposed methodology effectively uses existing geospatial data and prior knowledge about data acquisition to generate precise match pairs. First, the principal point of each image is directly georeferenced. The direct georeferencing is based on a novel new DEM-aided direct georeferencing algorithm which employs an iterative binary search in the elevation domain. Second, a terrain-adaptive search radius is calculated for each image based on the calculated ground points, POS data, and prior knowledge. Finally, initial match pairs are generated based on a camera-oriented approach. The camera-oriented approach generates match pairs using k nearest neighbours (knn) search and prior knowledge. False connections in the initial match pairs are filtered using geometrical constraints. A limited number of match pairs are kept to reduce the redundancy of the generated match graph. The proposed methodology is tested on a dataset containing 28,560 images. The experimental results show that the precision of the generated match pairs is 99.99%. An incremental 3D reconstruction of the scene is conducted based on the robustly matched images. More than 99.8% of all the images are successfully oriented. The profiling analysis shows that the proposed methodology generates all the match pairs in 7.719 seconds. The precision, efficiency, and robustness of the proposed methodology are comprehensively analysed. The experimental and simulation results show that the proposed methodology is efficient and precise for large-scale UAV photogrammetry.