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.
Highlights
With the availability of increasingly powerful SfM photogrammetry software and off-the shelf Unmanned Aircraft Systems (UAS) comes an increase of photogrammetric applications in geomorphologic research
SfM photogrammetry is applied in several areas of geomorphic research, including the study of mass movements, coastal erosion and fluvial environments
This paper describes the development of a geometric pre-calibration approach for the adopted UAS that is equipped with both optical and thermal sensors
Summary
With the availability of increasingly powerful SfM photogrammetry software and off-the shelf Unmanned Aircraft Systems (UAS) comes an increase of photogrammetric applications in geomorphologic research. SfM photogrammetry is applied in several areas of geomorphic research, including the study of mass movements, coastal erosion and fluvial environments. Surveys are cost-efficient and SfM photogrammetry from UAS allows repeat surveys to monitor earth surface processes. Modern SfM photogrammetry workflows detect and match keypoints and subsequently determine lens distortion parameters, camera position and orientation in a self-calibrating bundle adjustment. The algorithms approximate intrinsic and extrinsic camera parameters as an optimisation problem with a large numbers of variables. The simultaneous solution of several parameters can cause overparameterisation (James et al, 2017)
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