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Research Article

Fully automatic DOM generation method based on optical flow field dense image matching

ORCID Icon, , &
Pages 242-256 | Received 17 Aug 2022, Accepted 13 Dec 2022, Published online: 24 Jan 2023

Figures & data

Figure 1. Flowchart of digital orthophoto map (DOM) generation.

Figure 1. Flowchart of digital orthophoto map (DOM) generation.

Figure 2. Flowchart of 3D point cloud completion.

Figure 2. Flowchart of 3D point cloud completion.

Figure 3. Knot insertion of a B-spline curve.

Figure 3. Knot insertion of a B-spline curve.

Figure 4. 3D point clouds completion.

Figure 4. 3D point clouds completion.

Figure 5. Flowchart of the seamline network optimization.

Figure 5. Flowchart of the seamline network optimization.

Figure 6. Fully automatic extraction of the seamline network in a dense area of buildings.

Figure 6. Fully automatic extraction of the seamline network in a dense area of buildings.

Table 1. Parameters for the two sets of experimental images.

Figure 7. Doms of ten typical surface features generated by the fully automatic method proposed in this paper.

Figure 7. Doms of ten typical surface features generated by the fully automatic method proposed in this paper.

Figure 8. Mosaic accuracy curves and histograms between differential rectification images.

Figure 8. Mosaic accuracy curves and histograms between differential rectification images.

Table 2. DOM accuracy for the two experimental areas.

Figure 9. Visual comparison of the automatically generated DOMs on the Vahingen dataset.

Figure 9. Visual comparison of the automatically generated DOMs on the Vahingen dataset.

Table 3. Quantitative comparison of the three methods.

Data availability statement

Some data that support the results and analyses presented in this paper are freely available online (URLs: https://pan.baidu.com/s/1mQCQNjW9eGaesfxsYQqcaw Password: hsly). Others cannot be shared at this time, as the data also form part of an ongoing study.