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HAZARDOUS NOXIOUS SUBSTANCE DETECTION BASED ON HYPERSPECTRAL REMOTE SENSING TECHNIQUE

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dc.contributor.authorPark, Jae-Jin-
dc.contributor.authorPark, Kyung-Ae-
dc.contributor.authorFoucher, Pierre-Yves-
dc.contributor.authorDeliot, Philippe-
dc.contributor.authorLe Floch, Stephane-
dc.contributor.authorKim, Tae-Sung-
dc.contributor.authorOh, Sangwoo-
dc.contributor.authorLee, Moonjin-
dc.date.accessioned2022-10-18T00:35:06Z-
dc.date.available2022-10-18T00:35:06Z-
dc.date.created2022-10-12-
dc.date.issued2020-09-
dc.identifier.citationIGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, pp.2165-2168-
dc.identifier.issn2153-6996-
dc.identifier.urihttps://hdl.handle.net/10371/186411-
dc.description.abstractHazardous Noxious Substance (HNS) is transported entirely through large vessels, so there is always a potential risk of marine HNS spills. In the event of an HNS accident, it can cause enormous human and property damage, so prompt detection is required. However, there is a limit to human access by ship, we need to use remote sensing data. In this study, ground experiments using hyperspectral cameras were performed to construct a spectral library of HNS. We classified the HNS and non-HNS by applying the hyperspectral mixture algorithm, and presented the HNS detection probability for every pixel by calculating the spectrum-based abundance fraction. The results of this study are expected to be used to estimate the extent of HNS spill in the event of a marine HNS accident.-
dc.language영어-
dc.publisherIEEE-
dc.titleHAZARDOUS NOXIOUS SUBSTANCE DETECTION BASED ON HYPERSPECTRAL REMOTE SENSING TECHNIQUE-
dc.typeArticle-
dc.identifier.doi10.1109/IGARSS39084.2020.9324029-
dc.citation.journaltitleIGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM-
dc.identifier.wosid000664335302053-
dc.identifier.scopusid2-s2.0-85102014832-
dc.citation.endpage2168-
dc.citation.startpage2165-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorPark, Kyung-Ae-
dc.type.docTypeProceedings Paper-
dc.description.journalClass1-
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