Photogrammetric Point Clouds Generation from High-resolution Imagery in Urban Areas

Résumé du livre

Thirdly, effective dense matching propagation strategies are developed to obtain dense matches in urban images, including the local and regional propagation strategies. The dense matching will start from the previously obtained feature matches and the matching will be propagated from them to their neighbors based on the segment-based occlusion filtering, segment-adaptive similarity measurement, and other constraints, until all pixels in the images have been examined. From the obtained matching results, 3D point clouds are able to be generated through photogrammetric space intersection using the image orientation parameters. Experiment analysis using four sets of stereo images each representing different urban types are carried out. The accuracy of the 3D point clouds generated from the proposed approach is evaluated based on the comparison with the airborne LiDAR data. Comparisons are also made using the point clouds generated from the SGM (Semi-Global Matching) method. As evidenced by the experiment results, the proposed approach can generate not only the general geomorphological features of the surface, but also detailed features. The results from the quantitative accuracy comparison indicate that the proposed approach is able to generate 3D point clouds with a geometric accuracy comparable to LiDAR data, but offers a much higher point density. The proposed approach has similar performance with SGM in relatively flat areas, but shows a superiority to SGM in densely built-up areas. The developed approaches and methods presented in this research offer a viable alternative solution for 3D surface reconstruction in urban areas, and have the promise of facilitating the use of 3D data for other urban applications, such as urban planning, digital city construction, and smart city development.