Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/61635
Title: Automatic benthic habitat mapping using inexpensive underwater drones
Authors: Gauci, Adam
Deidun, Alan
Abela, John
Cachia, Ernest
Dimech, Sean
Keywords: Marine benthic ecology -- Malta
Benthic ecology -- Malta
Autonomous underwater vehicles
Marine resources conservation
Image analysis -- Data processing
Posidonia oceanica
Issue Date: 2020
Publisher: IEEE
Citation: Gauci, A., Deidun, A., Abela, J., Cachia, E., & Dimech, S. (2020). Automatic benthic habitat mapping using inexpensive underwater drones. IGARSS 2020, IEEE International Geoscience and Remote Sensing Symposium, Hawaii, TH2.R20.5., 2213-2216.
Abstract: The generation of benthic habitat maps relies either on direct in-situ observations made by SCUBA divers swimming in a rectilinear fashion, or on costly remote sensing techniques involving either ROVs or sonar technology. The recent commercialisation of off-the-shelf underwater drones has enhanced benthic mapping possibilities by providing a costeffective alternative. Despite still requiring ground truthing, such drones do not rely extensively on boat-support services. In this study, the applicability and feasibility of using an underwater high-resolution optical platform to automate the generation of benthic maps is investigated. A PowerVision PowerRay [1] was used to capture underwater imagery in an embayment along the north-east coast of the island of Malta (central Mediterranean). A Machine Learning method based on Self-Organizing Maps was then implemented to automate the classification process. Results produced from this technique were evaluated in terms of their accuracy through comparisons with a benthic habitat map of the same area that was generated through conventional means in a previous study.
URI: https://www.um.edu.mt/library/oar/handle/123456789/61635
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