Automatic seagrass detection: A survey
Document Type
Conference Proceeding
Publication Title
2017 International Conference on Electrical and Computing Technologies and Applications (ICECTA)
Publisher
IEEE
School
School of Science
RAS ID
26106
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
Access Rights
subscription content
Comments
Islam, S. M. S., Raza, S. K., Moniruzzamn, M., Janjua, N., Lavery, P., & Al-Jumaily, A. (2017, November). Automatic seagrass detection: A survey [Paper presentation]. 2017 International Conference on Electrical and Computing Technologies and Applications (ICECTA), Ras Al Khaimah, United Arab Emirates.
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