The use of underwater hyperspectral imaging deployed on remotely operated vehicles - methods and applications

Abstract: Currently a new underwater hyperspectral imager (UHI) have been deployed on Remotely Operated Vehicles (ROV) for a more automated identification, mapping and monitoring of bio-geo-chemical objects of interest (OOI). Sea floor maps based on UHI can be used to classify 001 based on specific optical fingerprints providing spectral upwelling radiance or reflectance with up to 1 nm spectral resolution in the visible range for each image pixel. Different habitats comprising soft bottom, deep and cold water coral reefs, sponge habitats, pipeline monitoring and kelp forest maps are examples for UHI-based mapping. Characterising material surface on man-made objects such as corrosion on pipelines and subsea structures and archaeological objects are other examples. The overall image quality and identification success of OOI can be optimized if movements of the ROV is controlled by a dynamic position (DP) system and corresponding speed, altitude, pitch, roll and yaw control. Likewise, illumination control is important to provide proper light intensity, spectral composition and illumination evenness of OOI to enhance data quality. The benefits of using UHI for seafloor habitat mapping can be evaluated by four categories of resolution. These are A) spatial resolution (image pixel size), B) spectral resolution (1-10 nm, 400-800 nm), C) radiometric resolution (dynamic range, bits per pixel), and D) temporal resolution for time-series and monitoring. These categories of resolution are discussed with respect to OOI identification and mapping using different case examples.

[1]  Wojciech M. Klonowski,et al.  Retrieving key benthic cover types and bathymetry from hyperspectral imagery , 2007 .

[2]  Hanumant Singh,et al.  Applications of Geo-Referenced Underwater Photo Mosaics in Marine Biology and Archaeology , 2007 .

[3]  Martin Ludvigsen,et al.  Dynamic positioning system for a small size ROV with experimental results , 2011, OCEANS 2011 IEEE - Spain.

[4]  Geir Johnsen,et al.  Biooptical characteristics of PSII and PSI in 33 species (13 pigment groups) of marine phytoplankton, and the relevance for pulse‐amplitude‐modulated and fast‐repetition‐rate fluorometry 1 , 2007 .

[5]  Asgeir J. Sørensen,et al.  Sea floor geometry approximation and altitude control of ROVs , 2014 .

[6]  Asgeir J. Sørensen,et al.  Development of dynamic positioning and tracking system for the ROV Minerva , 2012 .

[7]  Asgeir J. Sørensen,et al.  Scientific Operations Combining ROV and AUV in the Trondheim Fjord , 2014 .

[8]  Subsea optics and imaging , 2013 .

[9]  Bo-Cai Gao,et al.  Portable Remote Imaging Spectrometer coastal ocean sensor: design, characteristics, and first flight results. , 2014, Applied optics.

[10]  Geir Johnsen,et al.  Microscopic hyperspectral imaging used as a bio-optical taxonomic tool for micro- and macroalgae. , 2009, Applied optics.

[11]  E. Fry,et al.  Absorption spectrum (380-700 nm) of pure water. II. Integrating cavity measurements. , 1997, Applied optics.

[12]  Heidi M. Dierssen,et al.  Overview of hyperspectral remote sensing for mapping marine benthic habitats from airborne and underwater sensors , 2013, Optics & Photonics - Optical Engineering + Applications.

[13]  Martin Ludvigsen,et al.  Underwater hyperspectral imagery to create biogeochemical maps of seafloor properties , 2013 .

[14]  Zsolt Volent,et al.  Kelp forest mapping by use of airborne hyperspectral imager , 2007 .

[15]  Geir Johnsen,et al.  Phytoplankton Pigments: In vivo bio-optical properties of phytoplankton pigments , 2011 .