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Proceedings Paper

3D reconstruction optimization using imagery captured by unmanned aerial vehicles
Author(s): Abby L. Bassie; Sean Meacham; David Young; Gray Turnage; Robert J. Moorhead
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Paper Abstract

Because unmanned air vehicles (UAVs) are emerging as an indispensable image acquisition platform in precision agriculture, it is vitally important that researchers understand how to optimize UAV camera payloads for analysis of surveyed areas. In this study, imagery captured by a Nikon RGB camera attached to a Precision Hawk Lancaster was used to survey an agricultural field from six different altitudes ranging from 45.72 m (150 ft.) to 121.92 m (400 ft.). After collecting imagery, two different software packages (MeshLab and AgiSoft) were used to measure predetermined reference objects within six three-dimensional (3-D) point clouds (one per altitude scenario). In-silico measurements were then compared to actual reference object measurements, as recorded with a tape measure. Deviations of in-silico measurements from actual measurements were recorded as Δx, Δy, and Δz. The average measurement deviation in each coordinate direction was then calculated for each of the six flight scenarios. Results from MeshLab vs. AgiSoft offered insight into the effectiveness of GPS-defined point cloud scaling in comparison to user-defined point cloud scaling. In three of the six flight scenarios flown, MeshLab's 3D imaging software (user-defined scale) was able to measure object dimensions from 50.8 to 76.2 cm (20-30 inches) with greater than 93% accuracy. The largest average deviation in any flight scenario from actual measurements was 14.77 cm (5.82 in.). Analysis of the point clouds in AgiSoft (GPS-defined scale) yielded even smaller Δx, Δy, and Δz than the MeshLab measurements in over 75% of the flight scenarios. The precisions of these results are satisfactory in a wide variety of precision agriculture applications focused on differentiating and identifying objects using remote imagery.

Paper Details

Date Published: 16 May 2017
PDF: 8 pages
Proc. SPIE 10218, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II, 102180I (16 May 2017); doi: 10.1117/12.2254852
Show Author Affiliations
Abby L. Bassie, Mississippi State Univ. (United States)
Sean Meacham, Mississippi State Univ. (United States)
David Young, Mississippi State Univ. (United States)
Gray Turnage, Mississippi State Univ. (United States)
Robert J. Moorhead, Mississippi State Univ. (United States)

Published in SPIE Proceedings Vol. 10218:
Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II
J. Alex Thomasson; Mac McKee; Robert J. Moorhead, Editor(s)

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