Underwater Hyperspectral Imaging Using a Stationary Platform in the Trans-Atlantic Geotraverse Hydrothermal Field

Dumke, Ines, Ludvigsen, M., Ellefmo, S. L., Søreide, F., Johnsen, G. and Murton, B. J. (2018) Underwater Hyperspectral Imaging Using a Stationary Platform in the Trans-Atlantic Geotraverse Hydrothermal Field IEEE Transactions on Geoscience and Remote Sensing . DOI 10.1109/TGRS.2018.2878923.

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Underwater hyperspectral imaging is a relatively new method for characterizing seafloor composition. To date, it has been deployed from moving underwater vehicles, such as remotely operated vehicles and autonomous underwater vehicles. While moving vehicles allow relatively rapid surveying of several 10-1000 m², they are subjected to short-term variations in vehicle attitude that often compromise image acquisition and quality. In this study, we tested a stationary platform that was landed on the seabed and used an underwater hyperspectral imager (UHI) on a vertical swinging bracket. The imaged seafloor areas have dimensions of 2.3 m x 1 m and are characterized by very stable UHI data of high spatial resolution. The study area was the Trans-Atlantic Geotraverse hydrothermal field at the Mid-Atlantic Ridge (26° N) in water depths of 3530-3660 m. UHI data were acquired a 12 stations on an active and an inactive hydrothermal sulfide mound. Based on supervised classification, 24 spectrally different seafloor materials were detected, including hydrothermal and non-hydrothermal materials, and benthic fauna. The results show that the UHI data are able to spectrally distinguish different types of surface materials and benthic fauna in hydrothermal areas, and may therefore represent a promising tool for high-resolution seafloor exploration in potential future deep-sea mining areas.

Document Type: Article
Keywords: Image classification, remote sensing, spectroscopy, terrain mapping, underwater object detection, RRS James Cook
Research affiliation: OceanRep > GEOMAR > FB4 Dynamics of the Ocean Floor > FB4-GDY Marine Geodynamics
Refereed: Yes
DOI etc.: 10.1109/TGRS.2018.2878923
ISSN: 0196-2892
Projects: Blue Mining
Date Deposited: 08 Dec 2017 09:44
Last Modified: 07 Jan 2019 13:51
URI: http://eprints.uni-kiel.de/id/eprint/40476

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