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Automatic seagrass detection: A survey
Authors
A Al-Jumaily
SMS Islam
+4 more
N Janjua
P Lavery
M Moniruzzamn
SK Raza
Publication date
1 January 2017
Publisher
'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
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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OPUS - University of Technology Sydney
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oai:opus.lib.uts.edu.au:10453/...
Last time updated on 18/10/2019
Crossref
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info:doi/10.1109%2Ficecta.2017...
Last time updated on 06/08/2021