The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 935–943, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-935-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 935–943, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-935-2022
 
30 May 2022
30 May 2022

A COMBINED APPROACH FOR LONG-TERM MONITORING OF BENTHOS IN ANTARCTICA WITH UNDERWATER PHOTOGRAMMETRY AND IMAGE UNDERSTANDING

F. Menna1, E. Nocerino2, S. Malek1, F. Remondino1, and S. Schiaparelli3,4 F. Menna et al.
  • 13D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK), Trento, Italy
  • 2Dipartimento di Scienze Umanistiche e Sociali, Università degli Studi di Sassari, Sassari, Italy
  • 3DISTAV, University of Genoa, Genoa, Italy
  • 4Italian National Antarctic Museum (MNA, Section of Genoa), Genoa, Italy

Keywords: Underwater photogrammetry, change detection, long-term monitoring, antarctica, image segmentation, CNN

Abstract. Long-term monitoring projects are becoming more than ever crucial in assessing the effects of climate change on marine communities, especially in Antarctica, where these changes are expected to be particularly dramatic. Detailed studies of the Antarctic benthos are in fact particularly important for a better understanding of benthos dynamics and potential climate-driven shifts. Here, due to the extreme fragility of benthic communities, non-destructive techniques represent the best solution in long-term monitoring programs. In this paper we report new results from 2017, 2018, 2019 photogrammetric campaigns within the Italian National Antarctic Research Program (PNRA). A new protocol of data acquisition and multi-temporal processing that provides co-registered 3D point clouds between the three years without control points nor direct georeferencing methods is presented. This is achieved by adding a level of image understanding leveraging semantic segmentation with convolutional neural network (CNN) of the benthic features. Slow growing (estimated less than a mm per year) organisms, such as Corallinales (Rhodophyta algae), represent a natural stable pattern, leveraged to automatically orient in the same reference system the photogrammetric surveys of different epochs. This approach is also proved to be effective in improving the orientation of adjacent strips acquired within the same campaign. Within the paper an in depth analysis of the achieved results shows the effectiveness of the implemented procedure.