ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-1-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2021, 81–87, 2021
https://doi.org/10.5194/isprs-annals-V-1-2021-81-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2021, 81–87, 2021
https://doi.org/10.5194/isprs-annals-V-1-2021-81-2021

  17 Jun 2021

17 Jun 2021

ADAPTIVE WEIGHTING OF IMAGE OBSERVATIONS FOR SELF-CALIBRATION WITH FISHEYE IMAGES

L. F. Castanheiro1, A. M. G. Tommaselli1, M. B. Campos2, and A. Berveglieri1 L. F. Castanheiro et al.
  • 1Department of Cartography, São Paulo State University (UNESP) at Presidente Prudente, São Paulo 19060-900, Brazil
  • 2Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, Geodeetinrinne 2, FI-02430 Masala, Finland

Keywords: 360° fisheye cameras, observations weighting, hyperhemispherical lenses, stability constraints

Abstract. Fisheye cameras have been widely used in photogrammetric applications, but conventional techniques must be adapted to consider specific features of fisheye images, such as nonuniform resolution in the images. This work presents experimental results of an adaptive weighting of the observation in a self-calibrating bundle adjustment to cope with the nonuniform resolution of fisheye images. GoPro Fusion and Ricoh Theta dual-fisheye systems were calibrated with bundle adjustment based on equisolid-angle projection model combined with Conrady-Brown distortion model. The image observations were weighted as a function of radial distance based on combining loss of resolution and blurring in fisheye images. The results were compared with a similar trial by considering the same standard deviation for all image observations. The use of adaptive weighting of image observations reduced the estimated standard deviation of unit weight by 30 % and 50 % with GoPro Fusion and Ricoh Theta images, respectively. The estimation of relative orientation parameters (ROPs) was also improved (∼50 %) when using adaptive weighting for image observations.