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dc.contributor.authorSture, Øysteinen_GB
dc.contributor.authorFossum, Trygve Olaven_GB
dc.contributor.authorLudvigsen, Martinen_GB
dc.contributor.authorSyre Wiig, Martinen_GB
dc.date.accessioned2018-12-11T15:03:04Z
dc.date.accessioned2018-12-12T09:50:22Z
dc.date.available2018-12-11T15:03:04Z
dc.date.available2018-12-12T09:50:22Z
dc.date.issued2018
dc.identifier.citationSture ØS, Fossum TO, Ludvigsen M L, Syre Wiig MSW: Autonomous Optical Survey Based on Unsupervised Segmentation of Acoustic Backscatter. In: IEEE .. Proceedings of MTS/IEEE Oceans'18, Techno-Ocean 2018 - OTO'18, 2018. IEEEen_GB
dc.identifier.urihttp://hdl.handle.net/123456789/75033
dc.identifier.urihttp://hdl.handle.net/20.500.12242/2490
dc.descriptionSture, Øystein; Fossum, Trygve Olav; Ludvigsen, Martin; Syre Wiig, Martin. Autonomous Optical Survey Based on Unsupervised Segmentation of Acoustic Backscatter. I: Proceedings of MTS/IEEE Oceans'18, Techno-Ocean 2018 - OTO'18. IEEE 2018 ISBN 978-1-5386-1653-6. s. -en_GB
dc.description.abstractThe application of acoustics to study the seabed have for decades provided industry and science with valuable information, and is still excels in terms of spatial coverage and detail. An acoustic response from the seabed not only contains information about the range, through the two way travel time, but also the acoustic reflectivity of the substrate from the strength of the backscatter response. As the signal strength differs between substrate types, this information can be used to detect and classify different seabed types. However, there are ambiguities in the acoustic signatures and the reliance on ground truth samples, for succeeding in this identification, is a limiting factor. In this paper we present a way to mitigate this problem using Hidden Markov Random Fields (HMRF) to perform unsupervised segmentation of the backscatter response for the purpose of determining different seabed types. The outcome of this analysis is directly used to plan and conduct an autonomous near-seabed camera survey to verify the classification results, whilst complementing the acoustical data-set. The method is tested in a full-scale experiment and performed in-situ onboard a Kongsberg Hugin 1000 autonomous underwater vehicle (AUV).en_GB
dc.language.isoenen_GB
dc.subjectTermSet Emneord::Havbunn
dc.subjectTermSet Emneord::Akustikk
dc.subjectTermSet Emneord::Kartlegging
dc.titleAutonomous Optical Survey Based on Unsupervised Segmentation of Acoustic Backscatteren_GB
dc.typeArticleen_GB
dc.date.updated2018-12-11T15:03:04Z
dc.identifier.cristinID1641787
dc.identifier.cristinID1641787
dc.source.isbn978-1-5386-1653-6
dc.type.documentChapter


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