UAV Photogrammetry Accuracy Assessment for Corridor Mapping Based on the Number and Distribution of Ground Control Points

Abstract

Unmanned aerial vehicle (UAV) photogrammetry has recently emerged as a popular solution to obtain certain products necessary in linear projects, such as orthoimages or digital surface models. This is mainly due to its ability to provide these topographic products in a fast and economical way. In order to guarantee a certain degree of accuracy, it is important to know how many ground control points (GCPs) are necessary and how to distribute them across the study site. The purpose of this work consists of determining the number of GCPs and how to distribute them in a way that yields higher accuracy for a corridor-shaped study area. To do so, several photogrammetric projects have been carried out in which the number of GCPs used in the bundle adjustment and their distribution vary. The different projects were assessed taking into account two different parameters: the root mean square error (RMSE) and the Multiscale Model to Model Cloud Comparison (M3C2). From the different configurations tested, the projects using 9 and 11 GCPs (4.3 and 5.2 GCPs km−1, respectively) distributed alternatively on both sides of the road in an offset or zigzagging pattern, with a pair of GCPs at each end of the road, yielded optimal results regarding fieldwork cost, compared to projects using similar or more GCPs placed according to other distributions.

Extending Multi-Beam Sonar with Structure from Motion Data of Shorelines for Complete Pool Bathymetry of Reservoirs

“…Others have used sUAV-based methods to monitor changes in coastline due to wave run up [18] and evaluate water resource infrastructures such as spillways [15]. Similar to this study, other studies have also evaluated various parameters on procedures and their effect on accuracy including altitude, ground control, camera angle, and others [5,[32][33][34][35][36]. This is not an exhaustive review, and many more examples of using sUAV data and SfM algorithms to generate terrain data and to explore the impact of various parameters and processes exist.…”

“…Research has shown that without RTK GPS and high-precision IMUs, accurately projecting point clouds to a reference datum requires GCPs [5]. We experimented with not including GCPs in a small, local area at Newcastle Dam and in mis-tagging a GCP in one image.…”

“…Until recently, images collected from sUAV platforms were not useful for photogrammetry because the position of the camera was not known to provide sufficient accuracy and the cameras used were not of high enough quality. New computation algorithms address both of these issues by using a large number of images combined with minimal ground truth data to generate accurate terrain models [4][5][6][7]. For example, Ruberti et al [8] used sUAV imagery to assess sea cliff stability, addressing challenges similar to those encountered when developing full-pool bathymetric maps, and there have been many other studies that used sUAV imagery to generate terrain models, though in different application areas without the issue related to long, thin models [9][10][11][12][13][14].…”

“…Although gridded flights are most common for collecting over typical sites, some jobs require long, thin models such as the shoreline surveys (discussed in this paper), roads, road cut slopes, levees, or canals [33]. The issue of GCP spacing and orientation along long, thin corridors has been the subject of some research which explored the expected accuracy with different GCP spacing and orientation along a relatively long stretch of road [5]. This work is similar to that presented here, but roads generally follow regular lines, without significant changes, which is different than a reservoir shoreline which has a more fractal geometry.…”

A Practical Cross-View Image Matching Method between UAV and Satellite for UAV-Based Geo-Localization

“…Satellites, airplanes, and unmanned aerial vehicles (UAVs) have been the main carriers in the acquisition of remote sensing images. In particular, the UAV has the advantages of strong maneuverability, convenient operation, being hardly influenced by cloud, strong data acquisition abilities [1][2][3], etc., which make it widely applied in various fields [4][5][6][7][8][9][10][11][12]. How to efficiently locate the UAV images without geo-tags and navigate the UAV when the positioning system (e.g., GPS) is not available still face great challenges that are expected to be solved.…”