Sea Fog Identification from GOCI Images Using CNN Transfer Learning Models
SCIE
SCOPUS
Cited 20 time in
WEB OF SCIENCE
Cited 25 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)
- Yang, Chan Su(양찬수)
-
Alternative Author(s)
- 전호군; 김승룡; Jonathan Edwin; 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
- 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.