| Abstract |
Fine 3D models provide the key spatial basic information for smart city construction. However, actors such as perspective changes and occlusion lead to inaccurate edges, holes, and blurry building facade textures in 3D models generated from aerial images. Ground images can effectively solve the problems of missing bottoms and regional occlusion in oblique photography modeling. Therefore, a lightweight aerial-ground image matching method optimized by multi-layer progressive feature alignment is proposed to achieve robust matching of aerial-ground images and provide certain technical support for urban modeling. A multi-layer progressive matching network optimization strategy is designed, utilizing the high-level feature maps of the EfficientNet-B3 pre-trained model for bidirectional matching by taking the intersection of the bidirectional matching as the initial matching point set. Based on initial matching point pairs,the RANSAC strategy is used to calculate the initial homography matrix,thereby using it to transform the ground image, to obtain an image with an approximate aerial perspective, which completes feature matching and gross error removal. For aerial and near-field perspective images, matching and optimization are carried out on the previous multi-layer feature maps. In calculating the matching of each layer's feature map, the position of the upper layer's matching point pairs is corrected on each layer's feature map, to ultimately obtain an accurate set of matching points. Experiments are conducted on eight sets of typical data, including aerial images captured by the drone DJI-MAVIC2 and ground images captured by handheld devices. The results demonstrate that the proposed method has good matching performance compared to SIFT，D2-net，DFM，and other methods，with an average 1.3x increase in the Number of Correct Matches NCM compared to the suboptimal method. © 2023, Editorial Office of Computer Engineering. All rights reserved. |
