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

被引:56
|
作者
Johnsen, Geir [1 ]
Ludvigsen, Martin [2 ]
Sorensen, Asgeir [2 ]
Aas, Lars Martin Sandvik [3 ]
机构
[1] Norwegian Univ Sci & Technol NTNU, Dept Biol, Ctr Autonomous Marine Operat & Syst AMOS, N-7491 Trondheim, Norway
[2] NTNU, Dept Marine Technol, Ctr Autonomous Marine Operat & Syst AMOS, N-7491 Trondheim, Norway
[3] Ecotone AS, Havnegata 9, N-7010 Trondheim, Norway
来源
IFAC PAPERSONLINE | 2016年 / 49卷 / 23期
关键词
Remotely Operated Vehicle (ROV); Underwater Hyrperspectral Imager (UHI); mapping of seafloor; habitat mapping; habitat monitoring; ecosystem management and decision making;
D O I
10.1016/j.ifacol.2016.10.451
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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 (001). Sea floor maps based on UHI can he 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 UHT 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 he 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 001 identification and mapping using different case examples. (C) 2016 IFAC (International Federation of automatic Control) Hosting by Elsevier Ltd. All rights reserved.
引用
收藏
页码:476 / 481
页数:6
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