Ground-Control Networks for Image Based Surface Reconstruction: An Investigation of Optimum Survey Designs Using UAV Derived Imagery and Structure-from-Motion Photogrammetry

Toby Tonkin,Nicholas Midgley

doi:10.3390/rs8090786

Toby Tonkin, Nicholas Midgley

Open Access

https://doi.org/10.3390/rs8090786

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Journal: Remote Sensing	Publication Date: Sep 21, 2016
Citations: 203	License type: CC BY 4.0

Affiliation: Nottingham Trent University, University of Derby

Abstract

The use of small UAV (Unmanned Aerial Vehicle) and Structure-from-Motion (SfM) with Multi-View Stereopsis (MVS) for acquiring survey datasets is now commonplace, however, aspects of the SfM-MVS workflow require further validation. This work aims to provide guidance for scientists seeking to adopt this aerial survey method by investigating aerial survey data quality in relation to the application of ground control points (GCPs) at a site of undulating topography (Ennerdale, Lake District, UK). Sixteen digital surface models (DSMs) were produced from a UAV survey using a varying number of GCPs (3-101). These DSMs were compared to 530 dGPS spot heights to calculate vertical error. All DSMs produced reasonable surface reconstructions (vertical root-mean-square-error (RMSE) of <0.2 m), however, an improvement in DSM quality was found where four or more GCPs (up to 101 GCPs) were applied, with errors falling to within the suggested point quality range of the survey equipment used for GCP acquisition (e.g., vertical RMSE of <0.09 m). The influence of a poor GCP distribution was also investigated by producing a DSM using an evenly distributed network of GCPs, and comparing it to a DSM produced using a clustered network of GCPs. The results accord with existing findings, where vertical error was found to increase with distance from the GCP cluster. Specifically vertical error and distance to the nearest GCP followed a strong polynomial trend (R2 = 0.792). These findings contribute to our understanding of the sources of error when conducting a UAV-SfM survey and provide guidance on the collection of GCPs. Evidence-driven UAV-SfM survey designs are essential for practitioners seeking reproducible, high quality topographic datasets for detecting surface change.

Highlights

The use of small Unmanned Aerial Vehicles (UAV) for acquiring aerial images is widespread across a range of scientific disciplines (e.g., [1,2,3,4,5,6])
When a digital surface models (DSMs) produced with a uniform distribution of ground control points (GCPs) (28 GCPs) was subtracted from a DSM produced using a clustered, suboptimal GCP distribution (10 GCPs), residuals were found to increase into the decimetre range (Figure 4a), exceeding 0.15 m in the extremities of the DSM
The GCP requirements of the SfM-Multi-View Stereopsis (MVS) workflow are investigated, providing a case study that can be used to inform the implementation of future UAV surveys

Summary

Introduction

The use of small Unmanned Aerial Vehicles (UAV) for acquiring aerial images is widespread across a range of scientific disciplines (e.g., [1,2,3,4,5,6]). A surge in the use of UAVs for image acquisition has occurred simultaneously with the development of the automated photogrammetric technique commonly referred to as Structure-from-Motion (SfM) with Multi-View Stereopsis (MVS) (e.g., [7,8,9,10,11]). A range of scientific disciplines use an integrated approach combining the use of UAV with SfM-MVS photogrammetry (e.g., [12,13,14,15]). Smith and Vericat [20]

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