Title

Automatic seagrass detection: A survey

Document Type

Conference Proceeding

Publisher

IEEE

School

School of Science

RAS ID

26106

Comments

Originally published as:

Islam, S.M.S., Raza, S.K., Moniruzzamn, M., Janjua, N., Lavery, P., & Al-Jumaily. (2017). Automatic seagrass detection: A survey. In the proceedings of the 2017 International Conference on Electrical and Computing Technologies and Applications (ICECTA). Ras Al Khaimah, United Arab Emirates.

Original article available here

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.

DOI

10.1109/ICECTA.2017.8252036

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