ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-1-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2020, 101–107, 2020
https://doi.org/10.5194/isprs-annals-V-1-2020-101-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2020, 101–107, 2020
https://doi.org/10.5194/isprs-annals-V-1-2020-101-2020

  03 Aug 2020

03 Aug 2020

LIDAR AND PHOTOGRAMMETRIC DATASETS INTEGRATION USING SUB-BLOCK OF IMAGES

E. Mitishita, F. Costa, and J. Centeno E. Mitishita et al.
  • Geodetic Sciences Graduate Program, Department of Geomatics, Federal University of Parana, UFPR – Centro Politécnico, Setor de Ciências da Terra, CEP 81.531-990, Curitiba, Paraná, Brazil

Keywords: Integrated Sensor Orientation, Direct Sensor Orientation, Bundle Adjustment, Photogrammetry, Lidar

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.