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https://www.um.edu.mt/library/oar/handle/123456789/108621
Title: | Automatic benthic habitat mapping using inexpensive underwater drones |
Authors: | Gauci, Adam Deidun, Alan Abela, John Cachia, Ernest Dimech, Sean |
Keywords: | Imaging systems in geophysics Geophysics Artificial intelligence Computational intelligence Remote sensing Oceanography -- Equipment and supplies Oceanography -- Research Underwater exploration Remote submersibles |
Issue Date: | 2020 |
Publisher: | Institute of Electrical and Electronics Engineers |
Citation: | Gauci, A., Deidun, A., Abela, J., Cachia, E., & Dimech, S. (2020). Automatic Benthic Habitat Mapping using Inexpensive Underwater Drones. IEEE International Geoscience and Remote Sensing Symposium, USA. 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 cost-effective 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/123456789/108621 |
ISBN: | 9781728163741 |
ISSN: | 21537003 |
Appears in Collections: | Scholarly Works - FacEngME |
Files in This Item:
File | Description | Size | Format | |
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Automatic_benthic_habitat_mapping_using_inexpensive_underwater_drones(2020).pdf Restricted Access | 5.68 MB | Adobe PDF | View/Open Request a copy |
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