The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 999–1008, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-999-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 999–1008, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-999-2020

  12 Aug 2020

12 Aug 2020

MEASURING ARTIFICIAL REEFS USING A MULTI-CAMERA SYSTEM FOR UNMANNED UNDERWATER VEHICLES

R. Rofallski1, C. Tholen2, P. Helmholz3, I. Parnum4, and T. Luhmann1 R. Rofallski et al.
  • 1Institute for Applied Photogrammetry and Geoinformatics, Jade University of Applied Sciences, Oldenburg, Germany
  • 2Dept. of Engineering Sciences, Jade University of Applied Sciences, Wilhelmshaven, Germany
  • 3Spatial Sciences, Curtin University, Bentley, Western Australia
  • 4Centre for Marine Science and Technology, Curtin University, Bentley, Western Australia

Keywords: Underwater Photogrammetry, ROV, Multi-Camera System, Automated Image Masking, Transfer Learning, Convolutional Neural Network

Abstract. Artificial reefs provide an efficient way to improve marine life abundance in the oceans, including growth on the structure itself. Photogrammetric methods provide suitable tools to measure marine growth. This paper focusses on cubic reefs placed in Western Australia. The capturing platform featured a photogrammetric multi-sensor system for unmanned underwater vehicles attached to a low-cost vehicle BlueROV2. The multi-sensor system and its photogrammetric data captured was calibrated, adjusted and analyzed employing a structure-from-motion processing pipeline. Novel automated image masking techniques were developed and applied to the data to significantly reduce noise in the derived dense point clouds. Results show improvements of signal to noise ratio of more than 50 %, while maintaining a complete representation of the observed artificial reef.