Deep Red-Tide Learning from GOCI Images

Title
Deep Red-Tide Learning from GOCI Images
Author(s)
김수미; 신지선; 백승재; 유주형
KIOST Author(s)
Kim, Soo Mee(김수미)Shin, Jisun(신지선)Baek, Seungjae(백승재)Ryu, Joo Hyung(유주형)
Publication Year
2018-11-04
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.
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/22905
Bibliographic Citation
Pan Ocean Remote Sensing Conference, 2018
Publisher
PORSEC
Type
Conference
Language
English
Publisher
PORSEC
Related Researcher
Research Interests

Coastal Remote Sensing,RS based Marine Surveillance System,GOCI Series Operation,연안 원격탐사,원격탐사기반 해양감시,천리안해양관측위성 운영

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