Islam, Syed M. S., Syed K. Raza, Md Moniruzzamn, Naeem Janjua, Paual Lavery, and Adel Al-Jumaily. 2017. “Automatic Seagrass Detection: A Survey.” Pp. 1–5 in 2017 International Conference on Electrical and Computing Technologies and Applications (ICECTA). doi: 10.1109/ICECTA.2017.8252036.
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.
Table 1.- Different techniques for seagrass identification.
