Assessment of sensor pre-calibration to mitigate systematic errors in SfM photogrammetric surveys

Remotely piloted airborne system (RPAS) based structure-from-motion (SfM) photogrammetry is a recognised tool in geomorphological applications. However, time constraints, methodological requirements and ignorance can easily compromise photogrammetric rigour in geomorphological fieldwork. Light RPAS mounted sensors often provide inherent low geometric stability and are thus typically calibrated on-the-job in a self-calibrating bundle adjustment. Solving interior (lens geometry) and exterior (position and orientation) camera parameters requires variation of sensor-object distance, view angles and surface geometry.

Deficient camera calibration can cause systematic errors resulting in final digital elevation model (DEM) deformation. The application of multi-sensor systems, common in geomorphological research, poses additional challenges. For example, the low contrast in thermal imagery of vegetated surfaces constrains image matching algorithms.

We present a pre-calibration workflow to separate sensor calibration and data acquisition that is optimized for geomorphological field studies. The approach is time-efficient (rapid simultaneous image acquisition), repeatable (permanent object), at survey scale to maintain focal distance, and on-site to avoid shocks during transport.

The presented workflow uses a stone building as a suitable 3D calibration structure (alternatively boulder or bridge) providing structural detail in visible (DJI Phantom 4 Pro) and thermal imagery (Workswell WIRIS Pro). The dataset consists of feature coordinates extracted from terrestrial laser scanner (TLS) scans (3D reference data) and imagery (2D calibration data). We process the data in the specialized software, vision measurement system (VMS) as benchmark and the widely applied commercial SfM photogrammetric software, Agisoft MetaShape (AM) as convenient alternative. Subsequently, we transfer the camera parameters to the application in an SfM photogrammetric dataset of a river environment to assess the performance of self- and pre-calibration using different image network configurations. The resulting DEMs are validated against GNSS reference points and by DEMs of difference. 

We achieved calibration accuracies below one-third (optical) and one-quarter (thermal) of a pixel. In line with the literature, our results show that self-calibration yields the smallest errors and DEM deformations using multi-scale and oblique datasets. Pre-calibration in contrast, yielded the lowest overall errors and performed best in the single-scale nadir scenario. VMS consistently performed better than AM, possibly because AM's software “black-box” is less customisable and does not allow purely marker-based calibration. Furthermore, we present findings regarding sensor stability based on a repeat survey.

We find that pre-calibration can improve photogrammetric accuracies in surveys restricted to unfavourable designs e.g. nadir-only (water refraction, sensor mount). It can facilitate the application of thermal sensors on surfaces less suited to self-calibration. Most importantly, multi-scale survey designs could potentially become redundant, thus shortening flight time or increasing possible areal coverage.