Sea Fog Identification from GOCI Images Using CNN Transfer Learning Models

Sea Fog Identification from GOCI Images Using CNN Transfer Learning Models

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 per...

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Main Authors: Ho-Kun Jeon, Seungryong Kim, Jonathan Edwin, Chan-Su Yang
Format: Article
Language:English
Published: MDPI AG 2020-02-01
Series:Electronics
Subjects:
sea fog
remote sensing
goci
classifciation
cnn
transfer learning
Online Access:https://www.mdpi.com/2079-9292/9/2/311
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spelling doaj-12024c5e609c434dbc06733a20253f032020-11-25T01:40:00ZengMDPI AGElectronics2079-92922020-02-019231110.3390/electronics9020311electronics9020311Sea Fog Identification from GOCI Images Using CNN Transfer Learning ModelsHo-Kun Jeon0Seungryong Kim1Jonathan Edwin2Chan-Su Yang3Marine Security and Safety Research Center, Korea Institute of Ocean Science & Technology, Busan 49111, KoreaMarine Security and Safety Research Center, Korea Institute of Ocean Science & Technology, Busan 49111, KoreaMarine Security and Safety Research Center, Korea Institute of Ocean Science & Technology, Busan 49111, KoreaMarine Security and Safety Research Center, Korea Institute of Ocean Science & Technology, Busan 49111, KoreaThis 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.https://www.mdpi.com/2079-9292/9/2/311sea fogremote sensinggociclassifciationcnntransfer learning
collection DOAJ
language English
format Article
sources DOAJ
author Ho-Kun Jeon
Seungryong Kim
Jonathan Edwin
Chan-Su Yang
spellingShingle Ho-Kun Jeon
Seungryong Kim
Jonathan Edwin
Chan-Su Yang
Sea Fog Identification from GOCI Images Using CNN Transfer Learning Models
Electronics
sea fog
remote sensing
goci
classifciation
cnn
transfer learning
author_facet Ho-Kun Jeon
Seungryong Kim
Jonathan Edwin
Chan-Su Yang
author_sort Ho-Kun Jeon
title Sea Fog Identification from GOCI Images Using CNN Transfer Learning Models
title_short Sea Fog Identification from GOCI Images Using CNN Transfer Learning Models
title_full Sea Fog Identification from GOCI Images Using CNN Transfer Learning Models
title_fullStr Sea Fog Identification from GOCI Images Using CNN Transfer Learning Models
title_full_unstemmed Sea Fog Identification from GOCI Images Using CNN Transfer Learning Models
title_sort sea fog identification from goci images using cnn transfer learning models
publisher MDPI AG
series Electronics
issn 2079-9292
publishDate 2020-02-01
description 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.
topic sea fog
remote sensing
goci
classifciation
cnn
transfer learning
url https://www.mdpi.com/2079-9292/9/2/311
work_keys_str_mv AT hokunjeon seafogidentificationfromgociimagesusingcnntransferlearningmodels
AT seungryongkim seafogidentificationfromgociimagesusingcnntransferlearningmodels
AT jonathanedwin seafogidentificationfromgociimagesusingcnntransferlearningmodels
AT chansuyang seafogidentificationfromgociimagesusingcnntransferlearningmodels
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