Sea Fog Identification from GOCI Images Using CNN Transfer Learning Models SCIE SCOPUS

Cited 9 time in WEB OF SCIENCE Cited 10 time in Scopus
Title
Sea Fog Identification from GOCI Images Using CNN Transfer Learning Models
Author(s)
Jeon, Ho-Kun; Kim, Seungryong; Edwin, Jonathan; Yang, Chan-Su
KIOST Author(s)
Jeon, Ho Kun(전호군)Yang, Chan Su(양찬수)
Publication Year
2020-02
Abstract
This study proposes an approaching method of identifying sea fog by using Geostationary Ocean Color Imager (GOCI) data through applying a Convolution Neural Network Transfer Learning (CNN-TL) model. In this study, VGG19 and ResNet50, pre-trained CNN models, are used for their high identification performance. The training and testing datasets were extracted from GOCI images for the area of coastal regions of the Korean Peninsula for six days in March 2015. With varying band combinations and changing whether Transfer Learning (TL) is applied, identification experiments were executed. TL enhanced the performance of the two models. Training data of CNN-TL showed up to 96.3% accuracy in matching, both with VGG19 and ResNet50, identically. Thus, it is revealed that CNN-TL is effective for the detection of sea fog from GOCI imagery.
ISSN
2079-9292
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/38752
DOI
10.3390/electronics9020311
Bibliographic Citation
ELECTRONICS, v.9, no.2, 2020
Publisher
MDPI
Subject
DAYTIME SEA; MODIS; ALGORITHM; STRATUS
Keywords
sea fog; remote sensing; GOCI; classifciation; CNN; transfer learning
Type
Article
Language
English
Document Type
Article
Publisher
MDPI
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