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Paper Abstract and Keywords
Presentation 2019-11-01 11:00
Sea Fog Classification from GOCI Images using CNN Transfer Learning Models
Ho-Kun Jeon, Jonathan Edwin, Seungryong Kim, Chan-Su Yang (KIOST) SANE2019-65
Abstract (in Japanese) (See Japanese page) 
(in English) This study provides an approaching method of classifying sea fog from Geostationary Ocean Color Image, an optical satellite of South Korea. Convolution Neural Network Transfer Learning (CNN-TL) model is used because of a higher classification ability than a single CNN. The CNN-TL model is combined with dataset VGG19 and ResNet50 which have high performance but less layer than other datasets. In classification with 3-bands training images, the CNN-TL shows 96.7% and 93.0% in VGG19 and ResNet50, respectively. On the other hand, only CNN with identical training images shows the accuracy of 85.3% in VGG19 and 52% in VGG19 and ResNet 50. The result can be used to automate local sea fog detection and prediction.
Keyword (in Japanese) (See Japanese page) 
(in English) Sea Fog / CNN / Classification / Transfer learning / Ocean color / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 255, SANE2019-65, pp. 87-90, Oct. 2019.
Paper # SANE2019-65 
Date of Issue 2019-10-24 (SANE) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF SANE2019-65

Conference Information
Committee SANE  
Conference Date 2019-10-31 - 2019-11-01 
Place (in Japanese) (See Japanese page) 
Place (in English) KOREA (Jeju) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) ICSANE2019 
Paper Information
Registration To SANE 
Conference Code 2019-10-SANE 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Sea Fog Classification from GOCI Images using CNN Transfer Learning Models 
Sub Title (in English)  
Keyword(1) Sea Fog  
Keyword(2) CNN  
Keyword(3) Classification  
Keyword(4) Transfer learning  
Keyword(5) Ocean color  
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1st Author's Name Ho-Kun Jeon  
1st Author's Affiliation Korea Institute of Ocean Science & Technology (KIOST)
2nd Author's Name Jonathan Edwin  
2nd Author's Affiliation Korea Institute of Ocean Science & Technology (KIOST)
3rd Author's Name Seungryong Kim  
3rd Author's Affiliation Korea Institute of Ocean Science & Technology (KIOST)
4th Author's Name Chan-Su Yang  
4th Author's Affiliation Korea Institute of Ocean Science & Technology (KIOST)
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Speaker Author-1 
Date Time 2019-11-01 11:00:00 
Presentation Time 20 minutes 
Registration for SANE 
Paper # SANE2019-65 
Volume (vol) vol.119 
Number (no) no.255 
Page pp.87-90 
#Pages 4 
Date of Issue 2019-10-24 (SANE) 

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