Mucilage Detection from Hyperspectral and Multispectral Satellite Data

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dc.authorid0000-0001-8609-5378
dc.contributor.authorAbaci, Bahri
dc.contributor.authorDede, Murat
dc.contributor.authorYuksel, Seniha Esen
dc.contributor.authorYilmaz, Mete
dc.date.accessioned2026-02-12T21:05:05Z
dc.date.available2026-02-12T21:05:05Z
dc.date.issued2022
dc.departmentBursa Teknik Üniversitesi
dc.descriptionConference on Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVIII -- APR 03-JUN 12, 2022 -- ELECTR NETWORK
dc.description.abstractMucilage also called sea snot or sea saliva is a collection of mucus-like organic matter found in the sea. Although not harmful in the beginning, when mucilage increases over time, it covers the sea creatures and forms thick layers in the sea. Its existence and long duration change the oxygen balance in the seas, reduce biodiversity, fisheries, and tourism. Since April 2021, mucilage has emerged as both an ecological and economical problem in Turkey, spreading over an area of kilometers, clogging the fishing nets, causing problems in marine vessels, and disrupting the industry. These findings indicate that mucilage monitoring, early detection, and intervention before the economic and ecological damages grow out of proportion is quite necessary. Through the analysis of satellite data, it is possible to observe the existence of mucilage as thin, extended layers of white substance. Therefore, in this work, we analyze the Sentinel-2 multispectral data and PRISMA hyperspectral data to detect the mucilage in the early stages through the use of both traditional as well as deep learning algorithms. Our results indicate that it is possible to detect mucilage from satellite data with high accuracy, saving time and money for the cleaning efforts.
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [121G085]
dc.description.sponsorshipSPIE
dc.description.sponsorshipThis work is supported by The Scientific and Technological Research Council of Turkey (TUBITAK), Project No: 121G085.
dc.identifier.doi10.1117/12.2622287
dc.identifier.isbn978-1-5106-5065-7
dc.identifier.isbn978-1-5106-5064-0
dc.identifier.issn0277-786X
dc.identifier.issn1996-756X
dc.identifier.scopus2-s2.0-85133546142
dc.identifier.scopusqualityQ4
dc.identifier.urihttps://doi.org/10.1117/12.2622287
dc.identifier.urihttps://hdl.handle.net/20.500.12885/6786
dc.identifier.volume12094
dc.identifier.wosWOS:000836382200036
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpie-Int Soc Optical Engineering
dc.relation.ispartofAlgorithms, Technologies, and Applications For Multispectral and Hyperspectral Imaging Xxviii
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260212
dc.subjectMucilage
dc.subjectSentinel-2
dc.subjectPRISMA
dc.subjectmultispectral
dc.subjecthyperspectral
dc.subjectdeep learning
dc.titleMucilage Detection from Hyperspectral and Multispectral Satellite Data
dc.typeConference Object

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