Positional Precision Analysis of Orthomosaics Derived from Drone Captured Aerial Imagery

Abstract

The advancement of drones has revolutionized the production of aerial imagery. Using a drone with its associated flight control and image processing applications, a high resolution orthorectified mosaic from multiple individual aerial images can be produced within just a few hours. However, the positional precision and accuracy of any orthomosaic produced should not be overlooked. In this project, we flew a DJI Phantom drone once a month over a seven-month period over Oak Grove Cemetery in Nacogdoches, Texas, USA resulting in seven orthomosaics of the same location. We identified 30 ground control points (GCPs) based on permanent features in the cemetery and recorded the geographic coordinates of each GCP on each of the seven orthomosaics. Analyzing the cluster of each GCP containing seven coincident positions depicts the positional precision of the orthomosaics. Our analysis is an attempt to answer the fundamental question, “Are we obtaining the same geographic coordinates for the same feature found on every aerial image mosaic captured by a drone over time?” The results showed that the positional precision was higher at the center of the orthomosaic compared to the edge areas. In addition, the positional precision was lower parallel to the direction of the drone flight.

Influence of altitude on tropical marine habitat classification using imagery from fixed‐wing, water‐landing UAVs

“…This would decrease edge effects on orthomosaics and increase classification accuracy, as often there is a decrease in accuracy along edges due to limited overlap. The centre of orthomosaics have a higher positional accuracy due to more overlapping images (Hung et al 2019).…”

Comparing sUAS Photogrammetrically-Derived Point Clouds with GNSS Measurements and Terrestrial Laser Scanning for Topographic Mapping

“…The horizontal and vertical accuracy reached was 0.20 cm and 3.5 cm, respectively, which is higher than what is allowed for many engineering applications, particularly in the vertical component (e.g., civil and surveying engineering). These limitations are similar for several other studies, where they solely compare the accuracy between a limited number of checkpoints and not the overall 3D model within the area mapped [25,26,[33][34][35][36][37][38][39][40][41][42][43]. The results achieved in these studies are highly variable requiring the need for thorough evaluations comprised of a greater sample.…”

“…Both of these mapping techniques offer high-accuracy positioning and are commonly used in topographic mapping. sUAS technology has been shown to compute earthwork quantities with high accuracy, [36,47], and extensive testing has been performed to illustrate the impact of GCPs [24][25][26][33][34][35][36][37][38][39][40][41][42][43], however, the accuracy of the resulting point clouds has yet to be tested extensively, which is one of the main contributions of this study: to evaluate the performance of a sUAS point cloud comprehensively against GNSS-RTK and TLS mapping methods at areas beyond the GCPs, where the accuracy level reflects how well the sUAS point cloud was fitted to a priori GCPs and does not reflect the overall accuracy in areas away from the GCPs. We intentionally surveyed a flat area since flat scenes are commonly encountered in the field, e.g., agriculture, civil engineering, and construction, which pose challenges for accurately determining vertical measurements.…”

Multi-Variant Accuracy Evaluation of UAV Imaging Surveys: A Case Study on Investment Area

“…The commercial off-the-shelf (COTS) software packages, focusing on UAV imagery applications in geomatics, are based on a large number of variously oriented digital images and work in the composed and often an autonomous processing chain [10,11,12]. They allow an automatic computation of spatial orientation of photos and the parameters of camera interior orientation with self-calibration [13] using the bundle-block adjustment (BBA) method, generation of dense point clouds [11], a 3D mesh model and, finally, the digital surface model (DSM) [10,14] and orthomosaic [15].…”