International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLIII-B2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 977–984, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-977-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 977–984, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-977-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  12 Aug 2020

12 Aug 2020

MITIGATING IMAGE RESIDUALS SYSTEMATIC PATTERNS IN UNDERWATER PHOTOGRAMMETRY

F. Menna1, E. Nocerino2, S. Ural3, and A. Gruen3 F. Menna et al.
  • 13D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK), Trento, Italy
  • 2LIS UMR 7020, Aix-Marseille Université, CNRS, ENSAM, Université De Toulon, Marseille, France
  • 3Institute of Theoretical Physics, ETH Zurich, Zurich, Switzerland

Keywords: underwater photogrammetry, systematic error compensation, image residual corrections, accuracy evaluation

Abstract. Systematic errors may result from the adoption of an incomplete functional model that is not able to properly incorporate all the effects involved in the image formation process. These errors very likely appear as systematic residual patterns in image observations and produce deformations of the photogrammetric model in object space. The Brown/Beyer model of self-calibration is often adopted in underwater photogrammetry, although it does not take into account the refraction introduced by the passage of the optical ray through different media, i.e. air and water. This reduces the potential accuracy of photogrammetry underwater. In this work, we investigate through simulations the depth-dependent systematic errors introduced by unmodelled refraction effects when both flat and dome ports are used. The importance of camera geometry to reduce the deformation in the object space is analyzed and mitigation measures to reduce the systematic patterns in image observations are investigated. It is shown how, for flat ports, the use of a stochastic approach, consisting in radial weighting of image observations, improves the accuracy in object space up to 50%. Iterative look-up table corrections are instead adopted to reduce the evident systematic residual patterns in the case of dome ports.