The Synergistic Effects of GCPs and Camera Calibration Models on UAV-SfM Photogrammetry
Abstract
1. Introduction
2. Methods
2.1. Basic Methods
2.2. Study Areas and Data
2.2.1. Study Areas
2.2.2. UAV Data Acquisition
- Image Data
- Control Survey Data
2.3. Camera Models
2.4. The Synergistic Effects of GCPs and Camera Models
2.4.1. Interaction Between the Number of GCPs and Camera Models
2.4.2. Interaction Between the Quality of GCPs and Camera Models
2.5. Performance Evaluation
3. Results
3.1. Effects of Camera Models
3.1.1. Camera Calibration Without GCPs
3.1.2. Effects of Camera Models on Terrain Modeling Accuracy
3.2. Interaction Between the Number of GCPs and Camera Models
3.2.1. Effects of GCP Number on Camera Calibration
3.2.2. Interaction Effects on Terrain Modeling Accuracy
3.3. Interaction Between the Quality of GCPs and Camera Models
3.3.1. Effects of GCP Quality on Camera Calibration
3.3.2. Interaction Effects on Terrain Modeling Accuracy
4. Discussion
4.1. Camera Model Selection Strategy
4.2. Interaction Between the Number of GCPs and Camera Models
4.3. Interaction Between the Quality of GCPs and Camera Models
5. Conclusions
- Without GCPs, camera model selection is critical for improving camera calibration and terrain modeling accuracy. The use of complex camera models can reduce the overall correlation between distortion parameters. Compared with the simple model such as Model A (with only distortion parameter F), complex camera models can improve terrain modeling accuracy by approximately 70% and mitigate the spatial correlation. Model C (with F, Cx, Cy, K1–K4, and P1–P4) achieves a balance between camera model complexity and accuracy, making it a practical choice for most applications.
- When GCPs are available, the number of GCPs has a more significant effect on the accuracy improvement than the camera models. Increasing the number of GCPs can reduce the correlation between distortion parameters and improve the performance of camera models, thus improving the terrain modeling accuracy by approximately 45% to 70%. At the same time, the camera model complexity does not influence the required number of GCPs.
- When the GCP number is fixed, an interaction exists between the quality of GCPs and camera model selection. High-quality GCPs effectively mitigate the correlation between distortion parameters, leading to enhancing camera calibration and terrain modeling accuracy, with the RMSE of complex camera models decreasing by approximately 45% to 65%. Meanwhile, on the premise of ensuring effective calibration, complex camera models reduce the requirement for GCP quality. In other words, a more complex camera model should be chosen when the GCP quality is low.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Parameters | Focal Length (F) | Principal Point (Cx, Cy) | Radial Distortion (K1, K2, K3, K4) | Tangential Distortion (P1, P2, P3, P4) | Aspect Ratio and Skew (B1, B2) | |
---|---|---|---|---|---|---|
Camera Model | ||||||
A | ✓ | |||||
B | ✓ | ✓ | ✓ | |||
C | ✓ | ✓ | ✓ | ✓ | ||
D | ✓ | ✓ | ✓ | ✓ | ✓ |
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Wang, Z.; Shi, L.; Li, J.; Dai, W.; Lu, W.; Li, M. The Synergistic Effects of GCPs and Camera Calibration Models on UAV-SfM Photogrammetry. Drones 2025, 9, 343. https://doi.org/10.3390/drones9050343
Wang Z, Shi L, Li J, Dai W, Lu W, Li M. The Synergistic Effects of GCPs and Camera Calibration Models on UAV-SfM Photogrammetry. Drones. 2025; 9(5):343. https://doi.org/10.3390/drones9050343
Chicago/Turabian StyleWang, Zixin, Leyan Shi, Jinzhou Li, Wen Dai, Wangda Lu, and Mengqi Li. 2025. "The Synergistic Effects of GCPs and Camera Calibration Models on UAV-SfM Photogrammetry" Drones 9, no. 5: 343. https://doi.org/10.3390/drones9050343
APA StyleWang, Z., Shi, L., Li, J., Dai, W., Lu, W., & Li, M. (2025). The Synergistic Effects of GCPs and Camera Calibration Models on UAV-SfM Photogrammetry. Drones, 9(5), 343. https://doi.org/10.3390/drones9050343