Detection of Visual Signatures of Marine Mammals and Fish within Marine Renewable Energy Farms using Multibeam Imaging Sonar

Journal Article

Title: Detection of Visual Signatures of Marine Mammals and Fish within Marine Renewable Energy Farms using Multibeam Imaging Sonar
Publication Date:
January 22, 2019
Journal: Journal of Marine Science and Engineering
Volume: 7
Issue: 1
Pages: 1-19
Publisher: MDPI
Affiliation:
Receptor:
Technology Type:

Document Access

Website: External Link
Attachment: Access File
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Citation

Francisco, F.; Sundberg, J. (2019). Detection of Visual Signatures of Marine Mammals and Fish within Marine Renewable Energy Farms using Multibeam Imaging Sonar. Journal of Marine Science and Engineering, 7(1), 1-19.
Abstract: 

Techniques for marine monitoring have been greatly evolved over the past decades, making the acquisition of environmental data safer, more reliable and more efficient. On the other hand, the marine renewable energy sector has introduced dissimilar ways of exploring the oceans. Marine energy is mostly harvested in murky and high energetic places where conventional data acquisition techniques are impractical. This new frontier on marine operations brings the need for finding new techniques for environmental data acquisition, processing and analysis. Modern sonar systems, operating at high frequencies, can acquire detailed images of the underwater environment. Variables such as occurrence, size, class and behavior of a variety of aquatic species of fish, birds, and mammals that coexist within marine energy sites can be monitored using imaging sonar systems. Although sonar images can provide high levels of detail, in most of the cases they are still difficult to decipher. In order to facilitate the classification of targets using sonar images, this study introduces a framework of extracting visual features of marine animals that would serve as unique signatures. The acoustic visibility measure (AVM) is here introduced as technique of identification and classification of targets by comparing the observed size with a standard value. This information can be used to instruct algorithms and protocols in order to automate the identification and classification of underwater targets using imaging sonar systems. Using image processing algorithms embedded in Proviwer4 and FIJI software, this study found that acoustic images can be effectively used to classify cod, harbour and grey seals, and orcas through their size, shape and swimming behavior. The sonar images showed that cod occurred as bright, 0.9 m long, ellipsoidal targets shoaling in groups. Harbour seals occurred as bright torpedo-like fast moving targets, whereas grey seals occurred as bulky-ellipsoidal targets with serpentine movements. Orca or larger marine mammals occurred with relatively low visibility on the acoustic images compared to their body size, which measured between 4 m and 7 m. This framework provide a new window of performing qualitative and quantitative observations of underwater targets, and with further improvements, this method can be useful for environmental studies within marine renewable energy farms and for other purposes.

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