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DC Field | Value | Language |
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dc.contributor.author | Gauci, Adam | - |
dc.contributor.author | Deidun, Alan | - |
dc.contributor.author | Abela, John | - |
dc.contributor.author | Cachia, Ernest | - |
dc.contributor.author | Dimech, Sean | - |
dc.date.accessioned | 2023-04-18T14:19:21Z | - |
dc.date.available | 2023-04-18T14:19:21Z | - |
dc.date.issued | 2020 | - |
dc.identifier.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. | en_GB |
dc.identifier.isbn | 9781728163741 | - |
dc.identifier.issn | 21537003 | - |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/108621 | - |
dc.description.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. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Imaging systems in geophysics | en_GB |
dc.subject | Geophysics | en_GB |
dc.subject | Artificial intelligence | en_GB |
dc.subject | Computational intelligence | en_GB |
dc.subject | Remote sensing | en_GB |
dc.subject | Oceanography -- Equipment and supplies | en_GB |
dc.subject | Oceanography -- Research | en_GB |
dc.subject | Underwater exploration | en_GB |
dc.subject | Remote submersibles | en_GB |
dc.title | Automatic benthic habitat mapping using inexpensive underwater drones | en_GB |
dc.type | conferenceObject | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.bibliographicCitation.conferencename | International Geoscience and Remote Sensing Symposium | en_GB |
dc.bibliographicCitation.conferenceplace | Waikoloa, United States. 26/09-02/10/2020. | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
dc.identifier.doi | 10.1109/IGARSS39084.2020.9324241 | - |
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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