Abstract
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.
Highlights
Point cloud generation from photogrammetry– remotely piloted aircraft systems (RPAS) at high spatial and temporal resolution and accuracy is of increasing importance for many applications [1,2].The improvement is such that photogrammetry–RPAS is a robust tool to retrieve topographic products such as digital elevation models (DEMs), triangle irregular networks (TINs), contour levels, and orthophotographs [3,4,5,6,7,8]
structure from motion (SfM) adjusts all bundles of all photos in such a way that the root mean square error (RMSE) in the projection of terrain points on the corresponding photos are minimized by the least squares method [13,14,15]
Taking into account the slope change, we propose that the most changing variable over its range and the variable that most influences the accuracy of a point cloud (PC) on asphalt is the modulation transfer function 50 (MTF50) of each photograph, followed by ground sample distance (GSD), overlapping, and focal length
Summary
Point cloud generation from photogrammetry– remotely piloted aircraft systems (RPAS) at high spatial and temporal resolution and accuracy is of increasing importance for many applications [1,2].The improvement is such that photogrammetry–RPAS is a robust tool to retrieve topographic products such as digital elevation models (DEMs), triangle irregular networks (TINs), contour levels, and orthophotographs [3,4,5,6,7,8]. Point cloud generation from photogrammetry– remotely piloted aircraft systems (RPAS) at high spatial and temporal resolution and accuracy is of increasing importance for many applications [1,2]. SfM adjusts all bundles of all photos in such a way that the root mean square error (RMSE) in the projection of terrain points on the corresponding photos are minimized by the least squares method [13,14,15]. This optimization problem is known as bundle adjustment [16,17]. This has been developed based on two important publications: Sensors 2018, 18, 3880; doi:10.3390/s18113880 www.mdpi.com/journal/sensors
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