PREDICTING THE ACCURACY OF PHOTOGRAMMETRIC 3D RECONSTRUCTION FROM CAMERA CALIBRATION PARAMETERS THROUGH A MULTIVARIATE STATISTICAL APPROACH
| Autor: | E. Borgogno Mondino, Giacomo Caporusso, Eufemia Tarantino, Alessandra Capolupo, Mirko Saponaro |
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| Rok vydání: | 2020 |
| Předmět: |
lcsh:Applied optics. Photonics
Structure-from-Motion (SfM) Unmanned Aerial Vehicles (UAVs) Structure-from-Motion (SfM) Calibration Principal Components Analysis (PCA) Predictive Analysis lcsh:T Predictive Analysis Computer science Calibration (statistics) Principal Components Analysis (PCA) 3D reconstruction Process (computing) lcsh:TA1501-1820 Function (mathematics) computer.software_genre lcsh:Technology Calibration Unmanned Aerial Vehicles (UAVs) Photogrammetry lcsh:TA1-2040 Structure from motion Data mining lcsh:Engineering (General). Civil engineering (General) computer Camera resectioning |
| Zdroj: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLIII-B2-2020, Pp 479-486 (2020) |
| ISSN: | 2194-9034 |
| DOI: | 10.5194/isprs-archives-xliii-b2-2020-479-2020 |
| Popis: | Several tools have been introduced to generate accurate 3D models. Among these, Unmanned Aerial Vehicles (UAVs) are an effective low-cost tool to go beyond on-fields effort limits since they allow to fly over areas difficult to reach and to reduce the time needed to collect and process photogrammetric pictures as well. Combining their versatility with Structure from Motion (SfM) techniques efficiency has provided a widely accessible approach to generate accurate photogrammetric products. However, the outcome resolution and coherences also depend on sensor traits. Therefore, UAVs are usually equipped with low-cost non-metric cameras, with the consequent requirement for a calibration procedure to increase the final 3D models accuracy. Although several researchers have highlighted the strong impact of camera calibration parameters on the photogrammetric outcomes, their linkage has not been explored yet. This paper is aimed at investigating their relationship and to propose a novel predicting function of 3D photogrammetric reconstruction accuracy. Such function was estimated thanks to the application of the Principal Components Analysis (PCA) technique. Four photogrammetric UAV flight surveys provided the input data of PCA while an extra dataset was used to validate the results. Once PCA was completed, a synthetic index was proposed and the coefficient of determination was calculated between the index and error components. Synthetic indices values for the various datasets were applied as baseline to detect a predictive function able to assess the northern and eastern error components with a deviation of 0.005 m and 0.003 m, respectively. The proposed approach shows promising and satisfying results for predicting 3D models accuracy. |
| Databáze: | OpenAIRE |
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