Sparse Point Cloud Guided Digital Surface Model Generation for Aerial Images

Objectives: Digital surface model is of great significance in the fields of real-life 3D modeling, smart city construction, natural resources management, geoscience research, and hydrology and water resources management. However, dense matching, as a core step in generating digital surface models, is prone to matching failures in regions with a lack of texture, disparity gap and inconsistent illumination. The sparse point cloud data with high accuracy and extensive coverage after aerial triangulation, which can be used as a priori information to improve the accuracy of dense matching results. Methods: First, this paper proposes a sparse point cloud guidance (SPCG) method for generating digital surface models of aerial images. The method aims to constrain the dense matching of images using sparse point cloud encrypted by aerial triangulation. The sparse point cloud guidance first selects stereo image pairs with good geometric configurations, high overlap, and extensive coverage. Then, the number of sparse points is extended by using the closest proximity clustering and pyramid propagation methods. Additionally, the matching cost of the extended points is optimized by using the improved Gaussian function to enhance the accuracy of the dense matching results. Finally, the sparse point cloud is fused with the dense matching point cloud to generate the digital surface model. Results: Experiments on simulated stereo images and real aerial stereo images show that the optimized semi-global matching by the SPCG method in this paper significantly improves the matching accuracy of the original semi-global matching algorithm and outperforms the semi-global matching optimized by the Gaussian method and the deep learning method, pyramid stereo matching network. Numerically, the percentages of disparity maps generated by semi-global matching with greater than 1, 2, or 3 pixels difference from the true disparities are 46.72%, 32.83%, or 27.32%, respectively, whereas the SPCG method decreases by 7.67%, 9.75%, or 10.28%, respectively, compared to the former. The experimental results of the multiview aerial images show that the SPCG method accurately generates the digital surface model of the whole survey area, and it is better than the digital surface model generated by the superior SURE software in both qualitative and quantitative aspects. Conclusions: Compared to the original dense matching, sparse point cloud-guided dense matching improves the matching accuracy in difficult matching regions such as weak textures, repetitive textures and depth discontinuities. In turn, high precision and high density point clouds are generated. A complete digital surface model is generated by the fusion of the densely matched point clouds. © 2023 Wuhan University. All rights reserved.