GOCI-II Ocean Color Algorithm Development Environment
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 양현 | - |
dc.contributor.author | 한희정 | - |
dc.contributor.author | 허재무 | - |
dc.contributor.author | 정재훈 | - |
dc.contributor.author | 이태경 | - |
dc.contributor.author | 허웅 | - |
dc.contributor.author | 곽성희 | - |
dc.date.accessioned | 2020-07-15T09:54:00Z | - |
dc.date.available | 2020-07-15T09:54:00Z | - |
dc.date.created | 2020-02-11 | - |
dc.date.issued | 2018-11-05 | - |
dc.identifier.uri | https://sciwatch.kiost.ac.kr/handle/2020.kiost/22901 | - |
dc.description.abstract | In 2019, Geostationary Ocean Color Imager– II (GOCI-II), the follow-up satellite sensor of GOCI, will be launched. GOCI-II will observe 2 more ocean color images (8 times for GOCI) a day for 5 more spectral bands (8 bands for GOCI) than GOCI. Also, the ground sample distance (GSD) of GOCI-II will be enhanced as 250 m (500 m for GOCI) and the number of its products will be increased to 26 (13 for GOCI). Consequentially, the amount of GOCI-II data will be exponentially increased due to significantly improving the remote-sensing capability of GOCI-II. For producing ocean color information from GOCI-II data, the new ocean color algorithms will be needed because the assigned spectral bands for GOCI-II are different from GOCI. For GOCI-II data processing, however, the existing ocean color algorithm development approach with no regard for the high-performance data processing will not be applied because very large data sets would be processed and distributed in real time. In this study, therefore, we developed a software development environment for facilitating the implementation of GOCI-II ocean color algorithm using the high-performance computing technology. The proposed environment supports various parallelism techniques such as open multi-processing (OpenMP), open computing language (OpenCL), and message passing interface (MPI). Also, there are 4 kinds of servers (i.e., data processing server, build server, account n GOCI. Also, the ground sample distance (GSD) of GOCI-II will be enhanced as 250 m (500 m for GOCI) and the number of its products will be increased to 26 (13 for GOCI). Consequentially, the amount of GOCI-II data will be exponentially increased due to significantly improving the remote-sensing capability of GOCI-II. For producing ocean color information from GOCI-II data, the new ocean color algorithms will be needed because the assigned spectral bands for GOCI-II are different from GOCI. For GOCI-II data processing, however, the existing ocean color algorithm development approach with no regard for the high-performance data processing will not be applied because very large data sets would be processed and distributed in real time. In this study, therefore, we developed a software development environment for facilitating the implementation of GOCI-II ocean color algorithm using the high-performance computing technology. The proposed environment supports various parallelism techniques such as open multi-processing (OpenMP), open computing language (OpenCL), and message passing interface (MPI). Also, there are 4 kinds of servers (i.e., data processing server, build server, account | - |
dc.description.uri | 1 | - |
dc.language | English | - |
dc.publisher | PORSEC | - |
dc.relation.isPartOf | 2018 Pan Ocean Remote Sensing Conference (PORSEC) | - |
dc.title | GOCI-II Ocean Color Algorithm Development Environment | - |
dc.type | Conference | - |
dc.citation.conferencePlace | US | - |
dc.citation.endPage | 114 | - |
dc.citation.startPage | 114 | - |
dc.citation.title | 2018 Pan Ocean Remote Sensing Conference (PORSEC) | - |
dc.contributor.alternativeName | 양현 | - |
dc.contributor.alternativeName | 한희정 | - |
dc.contributor.alternativeName | 허재무 | - |
dc.identifier.bibliographicCitation | 2018 Pan Ocean Remote Sensing Conference (PORSEC), pp.114 | - |
dc.description.journalClass | 1 | - |