Comparison of Machine and Deep Learning Methods for Mapping Sea Farms Using High-Resolution Satellite Image
DC Field | Value | Language |
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dc.contributor.author | Choung, Yun-Jae | - |
dc.contributor.author | Jung, Donghwi | - |
dc.date.accessioned | 2022-03-15T07:42:44Z | - |
dc.date.available | 2022-03-15T07:42:44Z | - |
dc.date.created | 2022-03-14 | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 0749-0208 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/139045 | - |
dc.description.abstract | Previous research had shown that the supervised machine learning approach performed better than unsupervised machine learning for mapping sea farms using a high-resolution satellite image. The present work compares a support vector machine (SVM), which represents the supervised machine learning approach, and a deep neural network (DNN), which represents the deep learning approach, for mapping sea farms using KOMPSAT-3 satellite images acquired in the South Sea of South Korea. First, coastal maps were generated from the image source given by SVM and DNN. Next, the above-water and underwater farms were detected separately from both the maps based on the minimum and maximum thresholds. Finally, the detection accuracy of both the above-water and underwater farms from both coastal maps was assessed. Statistical results showed that deep learning (DNN) provided better performance than machine learning (SVM) for detecting above-water farms from the given high-resolution satellite image, while both DNN and SVM yielded the same performance for underwater farms. However, a few errors occurred in the detection because of the limitations of the pixel-based classification approaches. In future research, the deep learning algorithm combined with object-based classification, such as the convolutional neural network, can be used to detect sea farms from the given high-resolution image more accurately. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | COASTAL EDUCATION & RESEARCH FOUNDATION | - |
dc.title | Comparison of Machine and Deep Learning Methods for Mapping Sea Farms Using High-Resolution Satellite Image | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Jung, Donghwi | - |
dc.identifier.doi | 10.2112/JCR-SI114-085.1 | - |
dc.identifier.scopusid | 2-s2.0-85116916072 | - |
dc.identifier.wosid | 000723669300028 | - |
dc.identifier.bibliographicCitation | JOURNAL OF COASTAL RESEARCH, v.114, no.sp1, pp.420 - 423 | - |
dc.relation.isPartOf | JOURNAL OF COASTAL RESEARCH | - |
dc.citation.title | JOURNAL OF COASTAL RESEARCH | - |
dc.citation.volume | 114 | - |
dc.citation.number | sp1 | - |
dc.citation.startPage | 420 | - |
dc.citation.endPage | 423 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Environmental Sciences & Ecology | - |
dc.relation.journalResearchArea | Physical Geography | - |
dc.relation.journalResearchArea | Geology | - |
dc.relation.journalWebOfScienceCategory | Environmental Sciences | - |
dc.relation.journalWebOfScienceCategory | Geography, Physical | - |
dc.relation.journalWebOfScienceCategory | Geosciences, Multidisciplinary | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | sea farm | - |
dc.subject.keywordAuthor | satellite image | - |
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