U-Net Convolutional Neural Network Model for Deep Red Tide Learning Using GOCI SCIE SCOPUS

Cited 4 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)Shin, Jisun(신지선)Baek, Seungjae(백승재)Ryu, Joo Hyung(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
Publisher
COASTAL EDUCATION & RESEARCH FOUNDATION
Related Researcher
Research Interests

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

Files in This Item:
There are no files associated with this item.

qrcode

Items in ScienceWatch@KIOST are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse