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Variables Influencing the Accuracy of 3D Modeling of Existing Roads Using Consumer Cameras in Aerial Photogrammetry

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TargetPix, 18015 Granada, Spain
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Department Architectural and Engineering Graphic Expression, University of Granada, 18071 Granada, Spain
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Department Cartographic, Geodesic and Photogrammetric Engineering, University of Jaén, 23071 Jaén, Spain
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Author to whom correspondence should be addressed.
Sensors 2018, 18(11), 3880; https://doi.org/10.3390/s18113880
Received: 18 September 2018 / Revised: 27 October 2018 / Accepted: 4 November 2018 / Published: 11 November 2018
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
Point cloud (PC) generation from photogrammetry–remotely piloted aircraft systems (RPAS) at high spatial and temporal resolution and accuracy is of increasing importance for many applications. For several years, photogrammetry–RPAS has been used to recover civil engineering works such as digital elevation models (DEMs), triangle irregular networks (TINs), contour levels, orthophotographs, etc. This study analyzes the influence of variables involved in the accuracy of PC generation over asphalt shapes and determines the most influential variable based on the development of an artificial neural network (ANN) with patterns identified in the test flights. The input variables were those involved, and output was the three-dimension root mean square error (3D-RMSE) of the PC in each ground control point (GCP). The result of the study shows that the most influential variable over PC accuracy is the modulation transfer function 50 (MTF50). In addition, the study obtained an average 3D-RMSE of 1 cm. The results can be used by the scientific and civil engineering communities to consider MTF50 variables in obtaining images from RPAS cameras and to predict the accuracy of a PC over asphalt based on the ANN developed. Also, this ANN could be the beginning of a large database containing patterns from several cameras and lenses in the world market. View Full-Text
Keywords: photogrammetry; camera; accuracy model; modulation transfer function; neural network photogrammetry; camera; accuracy model; modulation transfer function; neural network
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González-Quiñones, J.J.; Reinoso-Gordo, J.F.; León-Robles, C.A.; García-Balboa, J.L.; Ariza-López, F.J. Variables Influencing the Accuracy of 3D Modeling of Existing Roads Using Consumer Cameras in Aerial Photogrammetry. Sensors 2018, 18, 3880.

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