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

TLDR

High‑resolution DEMs from consumer cameras on UAVs and ground platforms can achieve centimetric accuracy but often suffer systematic doming errors caused by near‑parallel imaging and inaccurate radial lens distortion correction, especially in SfM workflows with limited control points. The study demonstrates that self‑calibration within bundle adjustment produces erroneous radial distortion estimates and associated DEM error, and that incorporating oblique images can dramatically reduce this error. Simulations of multi‑image networks with near‑parallel viewing directions reveal that self‑calibration inflates radial distortion, while adding oblique imagery and applying practical flight‑plan solutions for fixed‑wing or rotor UAVs mitigates the error. Doming error scales linearly with radial distortion, enabling improved distortion estimation and reprocessing of existing datasets, and the approach is also applicable to ground‑based image capture. © 2014 The Authors; Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.

Abstract

ABSTRACT 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 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.