TY - JOUR

T1 - Mitigating systematic error in topographic models derived from UAV and ground-based image networks

AU - James, Michael

AU - Robson, Stuart

PY - 2014/8

Y1 - 2014/8

N2 - High 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 estricts 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 characterisation 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.

AB - High 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 estricts 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 characterisation 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.

KW - UAV

KW - DEM

KW - structure-from-motion

KW - bundle adjustment

KW - radial lens distortion

U2 - 10.1002/esp.3609

DO - 10.1002/esp.3609

M3 - Journal article

VL - 39

SP - 1413

EP - 1420

JO - Earth Surface Processes and Landforms

JF - Earth Surface Processes and Landforms

SN - 0197-9337

IS - 10

ER -