Deep Red-Tide Learning from GOCI Images
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김수미 | - |
dc.contributor.author | 신지선 | - |
dc.contributor.author | 백승재 | - |
dc.contributor.author | 유주형 | - |
dc.date.accessioned | 2020-07-15T09:54:07Z | - |
dc.date.available | 2020-07-15T09:54:07Z | - |
dc.date.created | 2020-02-11 | - |
dc.date.issued | 2018-11-04 | - |
dc.identifier.uri | https://sciwatch.kiost.ac.kr/handle/2020.kiost/22905 | - |
dc.description.abstract | GOCI is a geostationary ocean color satellite launched in 2011 and it provides hourly 8-bands spectral images of Northeast Asian region at 8 times a day with spatial resolution of 500 m. From GOCI images, it is possible to observe a broad ocean area for sensing red-tide occurrence, tidal movement changes and ocean disasters. In this study, we proposed a deep neural network model for automatic detection of red-tide occurrence from a GOCI image. We construct a big training dataset with GOCI nLw (normalized water leaving radiance) images and the corresponding red-tide index maps over 7 years (2011 – 2017). The red-tide index map, which indicates where the red-tide was occurred, was obtained by a decision tree based on the different spectral properties according to the chlorophyll content of red-tide species and the turbidity of the water. The decision tree classifies three red-tide types: i) having low chlorophyll, ii) high chlorophyll in Case-1 water, and iii) high chlorophyll in CASE-2 water of Korean sea area. The datasets are trained to extract some specific spectral features related to the red-tide by a deep neutral network model.cean area for sensing red-tide occurrence, tidal movement changes and ocean disasters. In this study, we proposed a deep neural network model for automatic detection of red-tide occurrence from a GOCI image. We construct a big training dataset with GOCI nLw (normalized water leaving radiance) images and the corresponding red-tide index maps over 7 years (2011 – 2017). The red-tide index map, which indicates where the red-tide was occurred, was obtained by a decision tree based on the different spectral properties according to the chlorophyll content of red-tide species and the turbidity of the water. The decision tree classifies three red-tide types: i) having low chlorophyll, ii) high chlorophyll in Case-1 water, and iii) high chlorophyll in CASE-2 water of Korean sea area. The datasets are trained to extract some specific spectral features related to the red-tide by a deep neutral network model. | - |
dc.description.uri | 1 | - |
dc.language | English | - |
dc.publisher | PORSEC | - |
dc.relation.isPartOf | Pan Ocean Remote Sensing Conference | - |
dc.title | Deep Red-Tide Learning from GOCI Images | - |
dc.type | Conference | - |
dc.citation.conferencePlace | KO | - |
dc.citation.title | Pan Ocean Remote Sensing Conference | - |
dc.contributor.alternativeName | 김수미 | - |
dc.contributor.alternativeName | 신지선 | - |
dc.contributor.alternativeName | 백승재 | - |
dc.contributor.alternativeName | 유주형 | - |
dc.identifier.bibliographicCitation | Pan Ocean Remote Sensing Conference | - |
dc.description.journalClass | 1 | - |