Abstract. High-resolution Digital Surface Models (DSMs) are crucial for diverse geospatial analyses and UAS photogrammetry offers a cost-effective option for DSM acquisition. However, corridor mapping, due to its linear geometry, challenges the extraction of 3D information without relying on Ground Control Points (GCPs). While onboard GNSS-RTK can improve accuracy, robust camera calibration is critical to mitigate systematic vertical errors propagating in derived DSM. Existing research lacks sufficient investigation into feasible pre-calibration strategies for corridor mapping without GCPs. Therefore, this study addresses this gap by evaluating the precision of DSMs obtained from five photogrammetric experiments without GCPs: one on-the-job calibration and four GNSS-Assisted Aerial Triangulation using on-site pre-calibrations with different sub-blocks of images. For precision assessment of DSMs, a reference experiment with 17 GCPs and all available images was also carried out. Our results show that including oblique images in on-site pre-calibration with sub-blocks significantly reduced the critical correlation between focal length and Z object-space coordinates (from 99% to less than 20%). That outcome directly influenced focal length estimation and allowed mitigation of vertical bias in generated DSMs. The results demonstrate that on-site pre-calibration notably improved the accuracy and precision of vertical spatial data acquisition. These findings highlight on-site oblique pre-calibration with a sub-block of images as a feasible and robust strategy for producing high-resolution 3D models in UAS corridor mapping, significantly reducing reliance on GCPs.
Building similarity graph...
Analyzing shared references across papers
Loading...
Pitombeira et al. (Fri,) studied this question.
synapsesocial.com/papers/69b5ff6e83145bc643d1bfea — DOI: https://doi.org/10.5194/isprs-annals-x-3-w4-2025-293-2026
Kalima Pitombeira
Universidade Federal do Paraná
Edson Aparecido Mitishita
Universidade Federal do Paraná
Charles Toth
The Ohio State University
ISPRS annals of the photogrammetry, remote sensing and spatial information sciences
Building similarity graph...
Analyzing shared references across papers
Loading...