Volume XLII-2/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W3, 25-31, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W3-25-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W3, 25-31, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W3-25-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

  23 Feb 2017

23 Feb 2017

THE EFFECT OF UNDERWATER IMAGERY RADIOMETRY ON 3D RECONSTRUCTION AND ORTHOIMAGERY

P. Agrafiotis1, G. I. Drakonakis1, A. Georgopoulos1, and D. Skarlatos2 P. Agrafiotis et al.
  • 1National Technical University of Athens, School of Rural and Surveying Engineering, Lab. of Photogrammetry Zografou Campus, 9 Heroon Polytechniou str., 15780, Zografou, Athens, Greece
  • 2Cyprus University of Technology, Civil Engineering and Geomatics Dept., Lab of Photogrammetric Vision 2-8 Saripolou str., 3036, Limassol, Cyprus

Keywords: Underwater 3D Reconstruction, Underwater Image Enhancement, SfM-MVS

Abstract. The work presented in this paper investigates the effect of the radiometry of the underwater imagery on automating the 3D reconstruction and the produced orthoimagery. Main aim is to investigate whether pre-processing of the underwater imagery improves the 3D reconstruction using automated SfM - MVS software or not. Since the processing of images either separately or in batch is a time-consuming procedure, it is critical to determine the necessity of implementing colour correction and enhancement before the SfM - MVS procedure or directly to the final orthoimage when the orthoimagery is the deliverable. Two different test sites were used to capture imagery ensuring different environmental conditions, depth and complexity. Three different image correction methods are applied: A very simple automated method using Adobe Photoshop, a developed colour correction algorithm using the CLAHE (Zuiderveld, 1994) method and an implementation of the algorithm described in Bianco et al., (2015). The produced point clouds using the initial and the corrected imagery are then being compared and evaluated.