Automatic seagrass detection: A survey

Syed M.S. Islam; Syed K. Raza; Md Moniruzzamn; Naeem Janjua; Paual Lavery; Adel Al-Jumaily

doi:10.1109/ICECTA.2017.8252036

Automatic seagrass detection: A survey

Syed M.S. Islam, Syed K. Raza, Md Moniruzzamn, Naeem Janjua, Paual Lavery, Adel Al-Jumaily

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	2017 International Conference on Electrical and Computing Technologies and Applications (ICECTA)
Place of Publication	United States of America
Publisher	Institute of Electrical and Electronics Engineers
Pages	527-531
Number of pages	5
ISBN (Electronic)	978-1-5386-0872-2
ISBN (Print)	978-1-5386-0873-9
DOIs	https://doi.org/10.1109/ICECTA.2017.8252036
Publication status	Published - Nov 2017
Externally published	Yes
Event	2017 International Conference on Electrical and Computing Technologies and Applications - Ras Al Khaimah, United Arab Emirates Duration: 21 Nov 2017 → 23 Nov 2017

Conference

Conference	2017 International Conference on Electrical and Computing Technologies and Applications
Abbreviated title	ICECTA 2017
Country/Territory	United Arab Emirates
City	Ras Al Khaimah
Period	21/11/17 → 23/11/17

Keywords

automated detection
Seagrass
underwater imagery

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10.1109/ICECTA.2017.8252036Licence: In Copyright

Cite this

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title = "Automatic seagrass detection: A survey",

abstract = "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.",

keywords = "automated detection, Seagrass, underwater imagery",

author = "Islam, {Syed M.S.} and Raza, {Syed K.} and Md Moniruzzamn and Naeem Janjua and Paual Lavery and Adel Al-Jumaily",

year = "2017",

month = nov,

doi = "10.1109/ICECTA.2017.8252036",

language = "English",

isbn = "978-1-5386-0873-9",

pages = "527--531",

booktitle = "2017 International Conference on Electrical and Computing Technologies and Applications (ICECTA)",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2017 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2017 ; Conference date: 21-11-2017 Through 23-11-2017",

}

Islam, SMS, Raza, SK, Moniruzzamn, M, Janjua, N, Lavery, P & Al-Jumaily, A 2017, Automatic seagrass detection: A survey. in 2017 International Conference on Electrical and Computing Technologies and Applications (ICECTA). Institute of Electrical and Electronics Engineers, United States of America, pp. 527-531, 2017 International Conference on Electrical and Computing Technologies and Applications, Ras Al Khaimah, United Arab Emirates, 21/11/17. https://doi.org/10.1109/ICECTA.2017.8252036

Automatic seagrass detection: A survey. / Islam, Syed M.S.; Raza, Syed K.; Moniruzzamn, Md et al.
2017 International Conference on Electrical and Computing Technologies and Applications (ICECTA). United States of America: Institute of Electrical and Electronics Engineers, 2017. p. 527-531.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Automatic seagrass detection

T2 - 2017 International Conference on Electrical and Computing Technologies and Applications

AU - Islam, Syed M.S.

AU - Raza, Syed K.

AU - Moniruzzamn, Md

AU - Janjua, Naeem

AU - Lavery, Paual

AU - Al-Jumaily, Adel

PY - 2017/11

Y1 - 2017/11

N2 - 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.

AB - 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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KW - Seagrass

KW - underwater imagery

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DO - 10.1109/ICECTA.2017.8252036

M3 - Conference contribution

AN - SCOPUS:85045988663

SN - 978-1-5386-0873-9

SP - 527

EP - 531

BT - 2017 International Conference on Electrical and Computing Technologies and Applications (ICECTA)

PB - Institute of Electrical and Electronics Engineers

CY - United States of America

Y2 - 21 November 2017 through 23 November 2017

ER -

Automatic seagrass detection: A survey

Abstract

Conference

Keywords

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