U-Net Convolutional Neural Network Model for Deep Red Tide Learning Using GOCI
SCIE
SCOPUS
Cited 20 time in
WEB OF SCIENCE
Cited 0 time in
Scopus
-
Title
- U-Net Convolutional Neural Network Model for Deep Red Tide Learning Using GOCI
-
Author(s)
- Kim, Soo Mee; Shin, Jisun; Baek, Seungjae; Ryu, Joo-Hyung
- KIOST Author(s)
- Kim, Soo Mee(김수미); Baek, Seung Jae(백승재); Ryu, Joo Hyung(유주형)
-
Alternative Author(s)
- 김수미; 신지선; 백승재; 유주형
-
Publication Year
- 2019-09
-
Abstract
- GOCI launched in 2010 is a geostationary satellite image sensor that monitors ocean color. It captures 8-band spectral satellite images of northeast Asian regions hourly, eight times a day. The spatial resolution of GOCI is about 500 m. GOCI is capable of monitoring a large ocean area for sensing various events such as red tide occurrences, tidal movement changes and ocean disasters. In this study, we propose a deep convolutional neural network model, U-Net, for automatic pixel-based detection of red tide occurrence from the spectral images captured by GOCI. We construct two training datasets with GOCI images and the corresponding red-tide index maps (RI maps) accumulated through 2011 to 2018. The RI maps indicate where red tides occurred and what kind of red tide species were there. U-Net consists of five U-shaped encoder and decoder layers to extract spectral features relating to red-tide species from GOCI images. We compared the performances of U-Nets trained from two datasets (i) consisting of only four spectral bands and (ii) consisting of all six spectral bands. The RI maps predicted by the trained U-Nets showed considerably matching spatial occurrence tendencies of three red tide species to the ground truths for validation images. The mean target accuracy with the four-band dataset was 13 % lower than that with the six-band dataset. The trained U-Net for pixel-wise red tide detection would be able to effectively inspect red tide occurrences in the huge area of water surrounding the Korean peninsula.
-
ISSN
- 0749-0208
-
URI
- https://sciwatch.kiost.ac.kr/handle/2020.kiost/759
-
DOI
- 10.2112/SI90-038.1
-
Bibliographic Citation
- JOURNAL OF COASTAL RESEARCH, v.90, no.sp1, pp.302 - 309, 2019
-
Publisher
- COASTAL EDUCATION & RESEARCH FOUNDATION
-
Keywords
- Red tide detection; GOCI; convolutional neural network (CNN); U-Net
-
Type
- Article
-
Language
- English
-
Document Type
- Article
- Files in This Item:
-
There are no files associated with this item.
Items in ScienceWatch@KIOST are protected by copyright, with all rights reserved, unless otherwise indicated.