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Open AccessArticle

Sea Fog Identification From GOCI Images Using CNN Transfer Learning Models

1
Marine Security and Safety Research Center, Korea Institute of Ocean Science & Technology, Busan 49111, Korea
2
Applied Ocean Science, University of Science & Technology, Daejeon 34113, Korea
3
Department of Convergence Study on the Ocean Science and Technology, Ocean Science and Technology School, Korea Maritime and Ocean University, Busan 49111, Korea
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(2), 311; https://doi.org/10.3390/electronics9020311
Received: 4 January 2020 / Revised: 27 January 2020 / Accepted: 4 February 2020 / Published: 11 February 2020
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.
Keywords: sea fog; remote sensing; GOCI; classifciation; CNN; transfer learning sea fog; remote sensing; GOCI; classifciation; CNN; transfer learning
MDPI and ACS Style

Jeon, H.-K.; Kim, S.; Edwin, J.; Yang, C.-S. Sea Fog Identification From GOCI Images Using CNN Transfer Learning Models. Electronics 2020, 9, 311.

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