Surface sediment classification using a deep learning model and unmanned aerial vehicle data of tidal flats SCIE SCOPUS
DC Field | Value | Language |
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dc.contributor.author | Kim, Kye-Lim | - |
dc.contributor.author | Woo, Han Jun | - |
dc.contributor.author | Jou, Hyeong Tae | - |
dc.contributor.author | Jung, Hahn Chul | - |
dc.contributor.author | Lee, Seung-Kuk | - |
dc.contributor.author | Ryu, Joo Hyung | - |
dc.date.accessioned | 2023-12-04T05:30:00Z | - |
dc.date.available | 2023-12-04T05:30:00Z | - |
dc.date.created | 2023-12-01 | - |
dc.date.issued | 2024-01 | - |
dc.identifier.issn | 0025-326X | - |
dc.identifier.uri | https://sciwatch.kiost.ac.kr/handle/2020.kiost/44872 | - |
dc.description.abstract | This study proposes a deep learning model, U-Net, to improve surface sediment classification using high-resolution unmanned aerial vehicle (UAV) images. We constructed training datasets with UAV images and corresponding labeling data acquired from three field surveys on the Hwangdo tidal flat. The labeling data indicated the distribution of surface sediment types. We compared the performance of the U-Net model trained in various implementation environments, such as surface sediment criteria, input datasets, and classification models. The U-Net trained with five class criteria—derived from previous classification criteria—yielded valid results (overall accuracy:65.6 %). The most accurate results were acquired from trained U-Net with all input datasets; in particular, the tidal channel density caused a significant increase in accuracy. The accuracy of the U-Net was approximately 20 % higher than that of other classification models. These results demonstrate that surface sediment classification using UAV images and the U-Net model is effective. | - |
dc.description.uri | 1 | - |
dc.language | English | - |
dc.publisher | Pergamon Press Ltd. | - |
dc.title | Surface sediment classification using a deep learning model and unmanned aerial vehicle data of tidal flats | - |
dc.type | Article | - |
dc.citation.title | Marine Pollution Bulletin | - |
dc.citation.volume | 198 | - |
dc.contributor.alternativeName | 김계림 | - |
dc.contributor.alternativeName | 우한준 | - |
dc.contributor.alternativeName | 주형태 | - |
dc.contributor.alternativeName | 유주형 | - |
dc.identifier.bibliographicCitation | Marine Pollution Bulletin, v.198 | - |
dc.identifier.doi | 10.1016/j.marpolbul.2023.115823 | - |
dc.identifier.scopusid | 2-s2.0-85178346870 | - |
dc.identifier.wosid | 001128411500001 | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.subject.keywordPlus | GRAIN-SIZE | - |
dc.subject.keywordPlus | INTERTIDAL SEDIMENTS | - |
dc.subject.keywordPlus | EXTRACTION | - |
dc.subject.keywordPlus | IMAGERY | - |
dc.subject.keywordPlus | NETWORKS | - |
dc.subject.keywordPlus | SEA | - |
dc.subject.keywordAuthor | Deep learning model | - |
dc.subject.keywordAuthor | Patch-wise U-Net | - |
dc.subject.keywordAuthor | UAV | - |
dc.subject.keywordAuthor | Surface sediment classification | - |
dc.subject.keywordAuthor | Tidal flats | - |
dc.relation.journalWebOfScienceCategory | Environmental Sciences | - |
dc.relation.journalWebOfScienceCategory | Marine & Freshwater Biology | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Environmental Sciences & Ecology | - |
dc.relation.journalResearchArea | Marine & Freshwater Biology | - |