Variables Influencing the Accuracy of 3D Modeling of Existing Roads Using Consumer Cameras in Aerial Photogrammetry
Metadatos
Afficher la notice complèteAuteur
González-Quiñones, Juan J.; Reinoso Gordo, Juan Francisco; León-Robles, Carlos A.; García-Balboa, José L.; Ariza-López, Francisco J.Editorial
MDPI
Materia
Photogrammetry Camera Accuracy model Modulation transfer function Neural network
Date
2018-11-11Referencia bibliográfica
González-Quiñones, J.J. [et al.]. Variables Influencing the Accuracy of 3D Modeling of Existing Roads Using Consumer Cameras in Aerial Photogrammetry. Sensors 2018, 18, 3880.
Patrocinador
The article processing charge (APC) was funded by the Research Group “Ingeniería Cartográfica” (Grant No. PAIDI-TEP-164 from the Regional Government of Andalucía) from the University of Jaén.Résumé
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.