Abstract
ABSTRACTHigh resolution digital elevation models (DEMs) are increasingly produced from photographs acquired with consumer cameras, both from the ground and from unmanned aerial vehicles (UAVs). However, although such DEMs may achieve centimetric detail, they can also display systematic broad‐scale error that restricts their wider use. Such errors which, in typical UAV data are expressed as a vertical ‘doming’ of the surface, result from a combination of near‐parallel imaging directions and inaccurate correction of radial lens distortion. Using simulations of multi‐image networks with near‐parallel viewing directions, we show that enabling camera self‐calibration as part of the bundle adjustment process inherently leads to erroneous radial distortion estimates and associated DEM error. This effect is relevant whether a traditional photogrammetric or newer structure‐from‐motion (SfM) approach is used, but errors are expected to be more pronounced in SfM‐based DEMs, for which use of control and check point measurements are typically more limited. Systematic DEM error can be significantly reduced by the additional capture and inclusion of oblique images in the image network; we provide practical flight plan solutions for fixed wing or rotor‐based UAVs that, in the absence of control points, can reduce DEM error by up to two orders of magnitude. The magnitude of doming error shows a linear relationship with radial distortion and we show how characterization of this relationship allows an improved distortion estimate and, hence, existing datasets to be optimally reprocessed. Although focussed on UAV surveying, our results are also relevant to ground‐based image capture. © 2014 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.
Highlights
IntroductionUnmanned aerial vehicles (UAVs) and systems such as tethered blimps and kites, are being increasingly used to provide high resolution, detailed imagery and associated digital elevation models (DEMs) for surface process and geomorphological research (e.g. Gimenez et al, 2009; Marzolff and Poesen, 2009; Smith et al, 2009; Niethammer et al, 2010; d’OleireOltmanns et al, 2012; Harwin and Lucieer, 2012; Rosnell and Honkavaara, 2012; Fonstad et al, 2013; Hugenholtz et al, 2013)
The results of simulating a standard stereo image pair in which an invariant camera model has radial distortion error (Figure 2a, central panel) reproduced the symmetrical domed deformation observed by Wackrow and Chandler (2008, 2011), and contrasts with the negligible digital elevation models (DEMs) deformation produced with an invariant error-free camera (Figure 2a, left panel)
If adjustment of the camera model was allowed within the bundle adjustment, the presence of image measurement noise allows the self-calibration process to converge on a non-zero radial distortion term, with associated DEM deformation (Figure 2a, right panel), again, similar to that seen in stereo image pairs (Wackrow and Chandler, 2008)
Summary
Unmanned aerial vehicles (UAVs) and systems such as tethered blimps and kites, are being increasingly used to provide high resolution, detailed imagery and associated digital elevation models (DEMs) for surface process and geomorphological research (e.g. Gimenez et al, 2009; Marzolff and Poesen, 2009; Smith et al, 2009; Niethammer et al, 2010; d’OleireOltmanns et al, 2012; Harwin and Lucieer, 2012; Rosnell and Honkavaara, 2012; Fonstad et al, 2013; Hugenholtz et al, 2013). Rosnell and Honkavaara, 2012; Javernick et al, 2014), that can make data unsuitable for broader comparative studies or for modelling gradient-sensitive processes such as rainfall runoff. This fundamental drawback needs to be overcome in order to fully exploit future data from UAVs and from similar ground-based image networks. We show how such systematic DEM deformation is associated with processing image sets with dominantly parallel viewing directions, and is correlated with inaccuracies in modelling radial camera lens distortion.
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