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

Abstract

The application of structure from motion (SfM) photogrammetry for digital elevation model (DEM) and orthophoto generation from visible imagery enjoys ever-growing popularity in geomorphological research. Photogrammetry experts, however, urge that a rigorous approach is a prerequisite for reliable results-a requirement that may conflict with real-world survey. We present a method that unites the two disciplines, using the example of a challenging SfM photogrammetric survey at a Scottish river.Using simultaneous geometric pre-calibration of a multi-sensor remotely piloted aircraft system (RPAS), the method facilitates time-efficient topography mapping and the integration of other wavelengths to create orthophotos providing additional surface information. The approach utilizes an on-site 3D structure-for example, a building, as calibration object, by extracting coordinates of natural features from lidar scans and sensor imagery. We assess the workflow with specialized calibration software (VMS) and widely applied commercial SfM photogrammetric software (AM), using a DJI Phantom optical and a Workswell thermal sensor. We achieved calibration accuracies below one-third (optical) and one-quarter (thermal) of a pixel. Subsequently, we transfer the sensor parameters to pre-calibrate the SfM application and compare the results to a self-calibrated workflow. In a systematic experiment using the optical river survey dataset, we assess the effectiveness of pre-calibration, oblique imagery, scale variation and masking to mitigate systematic DEM errors.Opposing trends show between the calibration strategies. Decreasing network complexity (i.e., flying heights/view angles) improves pre-calibrated but compromises self-calibrated scenarios. Pre-calibrating (VMS) imagery from a single height (30 m nadir) yielded the best results. This finding could have implications for geomorphological surveys, in which single-scale datasets are widespread practice, despite the literature's urge towards more complex imaging networks. The self-calibrated results legitimise this insistence: The same dataset resulted in pronounced dome-shaped DEM distortion, indicating systematic errors, whereas additional flying heights and angles significantly improved the results.

Post‐wildfire coastal dunefield response using photogrammetry and satellite indices

“…After iterations of reducing uncertain tie‐points (Table 7), camera calibration coefficient correlations displayed high correlations between focal (f) and radial (k1–k3) lens distortion coefficients and between principal point shifts (Cx, Cy) and tangential (p1, p2) lens distortion coefficients (see error reports linked in the Supporting Information) suggesting potential over‐parameterisation. Although some parameters are acknowledged to have inherent correlations (Senn et al, 2022), it is suggested to remove individual parameters if their error exceeds the coefficient value (James et al, 2017). Camera calibration coefficients in this study show errors that are significantly lower than their coefficient values, with errors representing a miniscule fraction of the corrections applied.…”

“…Georeferencing was prioritised on control points in images where targets were centrally located to reduce edge distortions. There is uncertainty on the optimal camera calibration corrections for SfM surveys processed in MS due to variations in sensor capabilities and pre‐processing of JPEGs for geometric adjustments (Bertin et al, 2022; Cooper et al, 2021; Gonçalves et al, 2021; Senn et al, 2022). Camera optimisation parameters or internal orientation parameters (Table 7) were chosen after extensive testing and according to works using a similar sensor (Cooper et al, 2021; Zhou, Daakir, et al, 2020).…”

Exploring the Optimal 4D-SfM Photogrammetric Models at Plot Scale

“…In the survey stage, various factors, such as the camera used for the photograph, the quality of the photographs [15], the angle of the capture [16,17], the number of photographs captured [18], the distance [19], the overlap rate [20], the surface characteristics of the 2 of 17 object being photographed and the ambient light conditions, can affect the accuracy of SfM results [12]. More photos, high overlap rate and proper control of the shooting angle can result in higher accuracy of the acquired 3D model data [18,20].…”