Abstract
Abstract. Imagery and Lidar datasets have been used frequently to extract geoinformation. Datasets in the same mapping or geodetic frame is a fundamental condition for this application. Nowadays, Direct Sensor Orientation (DSO) can be considered as a mandatory technology to be used in the aerial photogrammetric survey. Although the DSO provides a high degree of automation process due to the GNSS/INS technologies, the accuracies of the obtained results from the imagery and Lidar surveys are dependent on the quality of a group of parameters that models accurately the user conditions of the system at the moment the job is performed. This paper shows the study that was performed to improve the tridimensional accuracies of the aerial imagery and Lidar datasets integration using the 3D photogrammetric intersection of single models (pairs of images) with Exterior Orientation Parameters (EOP) estimated from DSO. A Bundle Adjustment with additional parameters (BBA) of a small sub-block of images is used to refine the Interior Orientation Parameters (IOP) and EOP in the job condition. In the 3D photogrammetric intersection experiments using the proposed approach, the horizontal and vertical accuracies, estimated by the Root Mean Square Error (RMSE) of the 3D discrepancies from the Lidar checkpoints, increased around of 25% and 75% respectively.
Highlights
The integration of aerial imagery and Lidar datasets has improved the autonomous approaches for geo-information extraction from imagery
Improving the study performed by Costa et al, (2018), this paper shows the study that was performed to improve the tridimensional accuracies of integration of aerial images and Lidar datasets using the 3D photogrammetric intersection of single models and Exterior Orientation Parameters (EOP) estimated from direct sensor orientation
This study considers that the obtained results from a bundle adjustment with additional parameters can be used to compute the small variability of the Interior Orientation Parameters (IOP) and EOP in-flight conditions
Summary
The integration of aerial imagery and Lidar datasets has improved the autonomous approaches for geo-information extraction from imagery. Costa et al, (2018) performed studies to verify the viability of methodology to improve the accuracies of the integration of aerial imagery and Lidar datasets, using Lidar control points (LCPs) in Integrated Sensor Orientation (ISO). Improving the study performed by Costa et al, (2018), this paper shows the study that was performed to improve the tridimensional accuracies of integration of aerial images and Lidar datasets using the 3D photogrammetric intersection of single models and EOPs estimated from direct sensor orientation. The Lidar dataset is used as a ground control of position information for the imagery survey performed by Direct Sensor Orientation technology In this way, this study considers that the obtained results from a bundle adjustment with additional parameters can be used to compute the small variability of the IOP and EOP in-flight conditions. The following four sections contain information about the materials and methodology, results and discussions of the performed experiments, as well as the conclusion and recommendations for future work
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