Name
Title | ||
|---|---|---|
Optimal Self-Calibration Strategies in the Combined Bundle Adjustment of Aerial-Terrestrial Integrated Images |
Abstract | ||
|---|---|---|
Accurate combined bundle adjustment (BA) is a fundamental step for the integration of aerial and terrestrial images captured from complementary platforms. In traditional photogrammetry pipelines, self-calibrated bundle adjustment (SCBA) improves the BA accuracy by simultaneously refining the interior orientation parameters (IOPs), including lens distortion parameters, and the exterior orientation parameters (EOPs). Aerial and terrestrial images separately processed through SCBA need to be fused using BA. Thus, the IOPs in the aerial-terrestrial BA must be properly treated. On one hand, the IOPs in one flight should be identical for the same images in physics. On the other hand, the IOP adjustment in the cross-platform-combined BA may mathematically improve the aerial-terrestrial image co-registration degree in 3D space. In this paper, the impacts of self-calibration strategies in combined BA of aerial and terrestrial image blocks on the co-registration accuracy were investigated. To answer this question, aerial and terrestrial images captured from seven study areas were tested under four aerial-terrestrial BA scenarios: the IOPs for both aerial and terrestrial images were fixed; the IOPs for only aerial images were fixed; the IOPs for only terrestrial images were fixed; the IOPs for both images were adjusted. The cross-platform co-registration accuracy for the BA was evaluated according to independent checkpoints that were visible on the two platforms. The experimental results revealed that the recovered IOPs of aerial images should be fixed during the BA. However, when the tie points of the terrestrial images are comprehensively distributed in the image space and the aerial image networks are sufficiently stable, refining the IOPs of the terrestrial cameras during the BA may improve the co-registration accuracy. Otherwise, fixing the IOPs is the best solution. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3390/rs14091969 | REMOTE SENSING |
Keywords | DocType | Volume |
image orientation, UAVs, photogrammetry, data integration | Journal | 14 |
Issue | ISSN | Citations |
9 | 2072-4292 | 0 |
PageRank | References | Authors |
0.34 | 0 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Linfu Xie | 1 | 0 | 1.01 |
| Han Hu | 2 | 1 | 1.36 |
| Zhu Qing | 3 | 7 | 4.87 |
| Xiaoming Li | 4 | 517 | 60.22 |
| Xiang Ye | 5 | 0 | 0.34 |
| Renzhong Guo | 6 | 41 | 11.41 |
| Yeting Zhang | 7 | 0 | 1.01 |
| Xiaoqiong Qin | 8 | 0 | 0.34 |
| Weixi Wang | 9 | 1 | 5.77 |