Automatic seagrass detection: A survey

Islam, SMS; Raza, SK; Moniruzzamn, M; Janjua, N; Lavery, P; Al-Jumaily, A

Automatic seagrass detection: A survey

Islam, SMS Raza, SK Moniruzzamn, M Janjua, N Lavery, P Al-Jumaily, A

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Publication Type:: Conference Proceeding
Citation:: 2017 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2017, 2017, 2018-January pp. 1 - 5
Issue Date:: 2017-06-28

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Field	Value	Language
dc.contributor.author	Islam, SMS	en_US
dc.contributor.author	Raza, SK	en_US
dc.contributor.author	Moniruzzamn, M	en_US
dc.contributor.author	Janjua, N	en_US
dc.contributor.author	Lavery, P	en_US
dc.contributor.author	Al-Jumaily, A https://orcid.org/0000-0003-0297-2463	en_US
dc.date.available	2020-05-25T19:12:35Z
dc.date.issued	2017-06-28	en_US
dc.identifier.citation	2017 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2017, 2017, 2018-January pp. 1 - 5	en_US
dc.identifier.isbn	9781538608722	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132344
dc.description.abstract	© 2017 IEEE. Seagrass is an important component of the marine ecosystem and plays a vital role in preserving the water quality. The traditional approaches for sea grass identification are either manual or semi-automated, resulting in costlier, time consuming and tedious solutions. There has been an increasing interest in the automatic identification of seagrasses and this article provides a survey of automatic classification techniques that are based on machine learning, fuzzy synthetic evaluation model and maximum likelihood classifier along with their performance. The article classifies the existing approaches on the basis of image types (i.e. aerial, satellite, and underwater digital), outlines the current challenges and provides future research directions.	en_US
dc.relation.ispartof	2017 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2017	en_US
dc.relation.isbasedon	10.1109/ICECTA.2017.8252036	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Automatic seagrass detection: A survey	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-January	en_US
utslib.for	0907 Environmental Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	2018-January	en_US

Abstract:

© 2017 IEEE. Seagrass is an important component of the marine ecosystem and plays a vital role in preserving the water quality. The traditional approaches for sea grass identification are either manual or semi-automated, resulting in costlier, time consuming and tedious solutions. There has been an increasing interest in the automatic identification of seagrasses and this article provides a survey of automatic classification techniques that are based on machine learning, fuzzy synthetic evaluation model and maximum likelihood classifier along with their performance. The article classifies the existing approaches on the basis of image types (i.e. aerial, satellite, and underwater digital), outlines the current challenges and provides future research directions.

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http://hdl.handle.net/10453/132344