On-Site Geometric Calibration of Thermal and Optical Sensors for Uas Photogrammetry

Abstract

Abstract. UAS imagery has become a widely used source of information in geomorphic research. When photogrammetric methods are applied to quantify geomorphic change, camera calibration is essential to ensure accuracy of the image measurements. Insufficient self-calibration based on survey data can induce systematic errors that can cause DEM deformations. The typically low geometric stability of consumer grade sensors necessitates in-situ calibration, as the reliability of a lab based calibration can be affected by transport. In this research a robust on-site workflow is proposed that allows the time-efficient and repeatable calibration of thermal and optical sensors at the same time. A stone building was utilised as calibration object with TLS scans for reference. The approach was applied to calculate eight separate camera calibrations using two sensors (DJI Phantom 4 Pro and Workswell WIRIS pro), two software solutions (Vision Measurement System (VMS) and Agisoft Metashape) and two different subsets of images per sensor. The presented results demonstrate that the approach is suitable to determine camera parameters for pre-calibrating photogrammetric surveys.

On‐site geometric calibration of RPAS mounted sensors for SfM photogrammetric geomorphological surveys

“…The exported 3D reference feature coordinates and the 2D observation coordinates were used to determine the benchmark pre‐calibration in VMS by solving the collinearity equation (Brown, 1971) as described in Senn et al. (2020). In parallel, we performed a second pre‐calibration in AM by importing the 3D reference feature coordinates to generate camera parameters by self‐calibrating bundle adjustment (Figure 1).…”

“…The AM version used does not allow a marker‐based calibration; hence the photogrammetric tie points could not be excluded from the calibration, unlike the procedure presented by Senn et al. (2020). We believe a strictly marker‐based calibration would facilitate the exclusion of unwanted image‐based optimization in the AM software ‘black‐box‘ and at the same time allow a better comparability with VMS (Harwin et al 2015…”

“…The approach presented by Senn et al. (2020) meets the criteria but has not yet been applied to pre‐calibrate a survey.…”

“…A pre‐calibration method aimed at geomorphological applications was described by Senn et al. (2020) and was designed as an applicable addition that can fit into restricted fieldwork schedules. It overcomes the shortcomings of previous, typically lab‐based, calibration workflows, most prominently scale, workload and suitability for different sensors.…”

“…Following up on Senn et al. (2020), this paper reports on the effectiveness of sensor pre‐ and self‐calibration, as well as other error mitigation strategies, on the accuracy of results achieved with SfM photogrammetry in a full topographic survey of wetted and dry areas in a river corridor. To this end, we conducted a systematic experiment in which we used all combinations of calibration strategy (pre‐calibration in vision measurement system (VMS), pre‐calibration in Agisoft MetaShape (AM) and self‐calibration in AM), flight altitudes and viewing angles, as well as masking out error‐prone areas.…”