Sea Fog Classification from GOCI Images using CNN Transfer Learning Models
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 전호군 | - |
dc.contributor.author | Jonathan Edwin | - |
dc.contributor.author | 김승룡 | - |
dc.contributor.author | 양찬수 | - |
dc.date.accessioned | 2020-07-15T06:34:47Z | - |
dc.date.available | 2020-07-15T06:34:47Z | - |
dc.date.created | 2020-02-11 | - |
dc.date.issued | 2019-10-31 | - |
dc.identifier.uri | https://sciwatch.kiost.ac.kr/handle/2020.kiost/22075 | - |
dc.description.abstract | This study provides an approaching method of classifying sea fog from Geostationary Ocean Color Image, an optical satellite of South Korea. Convolution Neural Network Transfer Learning (CNN-TL) model is used because of a higher classification ability than a single CNN. The CNN-TL model is combined with dataset VGG19 and ResNet50 which have high performance but less layer than other datasets. In classification with 3-bands training images, the CNN-TL shows 96.7% and 93.0% in VGG19 and ResNet50, respectively. On the other hand, only CNN with identical training images shows the accuracy of 85.3% in VGG19 and 52% in VGG19 and ResNet 50. The result can be used to automate local sea fog detection and prediction. | - |
dc.description.uri | 1 | - |
dc.language | English | - |
dc.publisher | IEICE | - |
dc.relation.isPartOf | ICSANE 2019 | - |
dc.title | Sea Fog Classification from GOCI Images using CNN Transfer Learning Models | - |
dc.type | Conference | - |
dc.citation.conferencePlace | JA | - |
dc.citation.endPage | 90 | - |
dc.citation.startPage | 87 | - |
dc.citation.title | ICSANE 2019 | - |
dc.contributor.alternativeName | 전호군 | - |
dc.contributor.alternativeName | Jonathan Edwin | - |
dc.contributor.alternativeName | 김승룡 | - |
dc.contributor.alternativeName | 양찬수 | - |
dc.identifier.bibliographicCitation | ICSANE 2019, pp.87 - 90 | - |
dc.description.journalClass | 1 | - |