GOCI-II 자료처리의 구현 간소화를 지원하기 위한 해색 알고리즘 개발 환경
DC Field | Value | Language |
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dc.contributor.author | 양현 | - |
dc.contributor.author | 한희정 | - |
dc.contributor.author | 허재무 | - |
dc.contributor.author | 정재훈 | - |
dc.contributor.author | Taekyung Lee | - |
dc.contributor.author | Woong Hu | - |
dc.contributor.author | Sunghee Kwak | - |
dc.date.accessioned | 2020-07-15T12:33:21Z | - |
dc.date.available | 2020-07-15T12:33:21Z | - |
dc.date.created | 2020-02-11 | - |
dc.date.issued | 2018-05-10 | - |
dc.identifier.uri | https://sciwatch.kiost.ac.kr/handle/2020.kiost/23378 | - |
dc.description.abstract | In 2019, Geostationary Ocean Color Imager– II (GOCI-II) will be launched as the follow-up satellite sensor of GOCI. GOCI has observed ocean color images 8 times a day for 8 spectral bands. In terms of GOCI-II observations, on the other hand, 5 more spectral bands will be added and 2 more ocean color images a day will be obtained in order to widen the spectra range and increase the number of observations, respectively. Also, the spatial resolution of GOCI-II will be more precise as 250 m (500 m for GOCI) and its number of products will be increased to 26 (13 for GOCI). We expect that the remote-sensing capability of GOCI-II will be greatly improved than that of GOCI. However, the existing ocean color algorithm development methodology with no regard for the high-speed data processing will not be accepted because significantly large GOCI-II data would be processed and distributed in real time. In this study, therefore, we designed a software development environment for helping the implementation of GOCI-II ocean color algorithm using the high-performance computing schemes. We expect that GOCI-II ocean color algorithm developers will be able to easily implement their source codes using the various parallelism techniques such as open multi-processing (OpenMP), open computing language (OpenCL), and message passing interface (MPI) under the proposed software development environment. In addition, it will support that th hand, 5 more spectral bands will be added and 2 more ocean color images a day will be obtained in order to widen the spectra range and increase the number of observations, respectively. Also, the spatial resolution of GOCI-II will be more precise as 250 m (500 m for GOCI) and its number of products will be increased to 26 (13 for GOCI). We expect that the remote-sensing capability of GOCI-II will be greatly improved than that of GOCI. However, the existing ocean color algorithm development methodology with no regard for the high-speed data processing will not be accepted because significantly large GOCI-II data would be processed and distributed in real time. In this study, therefore, we designed a software development environment for helping the implementation of GOCI-II ocean color algorithm using the high-performance computing schemes. We expect that GOCI-II ocean color algorithm developers will be able to easily implement their source codes using the various parallelism techniques such as open multi-processing (OpenMP), open computing language (OpenCL), and message passing interface (MPI) under the proposed software development environment. In addition, it will support that th | - |
dc.description.uri | 1 | - |
dc.language | English | - |
dc.publisher | KSRS | - |
dc.relation.isPartOf | ISRS 2018 | - |
dc.title | GOCI-II 자료처리의 구현 간소화를 지원하기 위한 해색 알고리즘 개발 환경 | - |
dc.title.alternative | OCEAN COLOR ALGORITHM DEVELOPMENT ENVIRONMENT FOR SUPPORTING IMPLEMENTATION SIMPLIFICATION OF GOCI-II DATA PROCESSING | - |
dc.type | Conference | - |
dc.citation.conferencePlace | KO | - |
dc.citation.endPage | 3 | - |
dc.citation.startPage | 1 | - |
dc.citation.title | ISRS 2018 | - |
dc.contributor.alternativeName | 양현 | - |
dc.contributor.alternativeName | 한희정 | - |
dc.contributor.alternativeName | 허재무 | - |
dc.contributor.alternativeName | 정재훈 | - |
dc.identifier.bibliographicCitation | ISRS 2018, pp.1 - 3 | - |
dc.description.journalClass | 1 | - |