Parallel Development of Comparable Photogrammetric Workflows Based on UAV Data Inside SW Platforms

Abstract

A wide range of industrial applications benefits from the accessibility of image-based techniques for three-dimensional modelling of different multi-scale objects. In the last decade, along with the technological progress mainly achieved with the use of Unmanned Aerial Vehicles (UAVs), there has been an exponential growth of software platforms enabled to return photogrammetric products. On the other hand, the different levels of final product accuracy resulting from the adoption of different processing approaches in various softwares have not yet been fully understood. To date, there is no validation analysis in literature focusing on the comparability of such products, not even in relation to the use of workflows commonly allowed inside various software platforms. The lack of detailed information about the algorithms implemented in the licensed platforms makes the whole interpretation even more complex. This work therefore aims to provide a comparative evaluation of three photogrammetric softwares commonly used in the industrial field, in order to obtain coherent, if not exactly congruent results. After structuring the overall processing workflow, the processing pipelines were accurately parameterized to make them comparable in both licensed and open-source softwares. For the best interpretation of the results derived from the generation of point clouds processed by the same image dataset, the obtainable values of root-mean-square error (RMSE) were analyzed, georeferencing models as the number of GCPs varied. The tests carried out aimed at investigating the elements shared by the platforms tested, with the purpose of supporting future studies to define a unique index for the accuracy of final products.

Influence of Spatial Resolution for Vegetation Indices’ Extraction Using Visible Bands from Unmanned Aerial Vehicles’ Orthomosaics Datasets

“…Despite this, the scientific community was still engaged in finding optimised methodologies to mitigate the various uncertainties from lens distortions in the inertial measurement unit and the sensor's interior and exterior orientation parameters. However, several recent works attested to the validity of these products according to the scientific community's accuracy standards [13,14].…”

Influence of Co-Alignment Procedures on the Co-Registration Accuracy of Multi-Epoch SFM Points Clouds

“…A processing workflow (Figure 2), consistent with the outcomes of previous works (Saponaro et al, 2020c), was planned to handle photogrammetric imagery, generating accurate point clouds and Digital Elevation Models (DEMs). It was run independently in three separate chunks (DEC, MAR, OCT), each of them created to process a specific dataset built from a corresponding flight mission.…”

“…The multi-chunks processing was performed in parallel in Agisoft Metashape (v.1.5.2) software on an Intel(R) Core (TM) i7-3970X CPU 3.50GHz workstation, featuring 16GB of RAM and an NVIDIA GeForce GTX 650 graphic cards. The Workspace Setting is aimed at ensuring the plausibility of each subsequent operation (Saponaro et al, 2020c). This step is mainly based on the definition of a reference system, needed to guarantee the consistency of orientation and scale of the final models, and camera calibration parameters, required to drastically reduce final error.…”

“…In Agisoft Metashape platform, selecting the Gradual Selection item sifts through several filtering options. Table 1 shows the filtering manipulations carried out on the sparse point clouds, according to (Saponaro et al, 2020c).…”

“…In the case of DG, the position information recorded in each image by the RPAS receiver were useful, even if they transferred less sizeable corrections due to their lower accuracy. The chosen parameterization for the performed computations is reported in Table 1, according to (Saponaro et al, 2020c).…”