Sea Fog Classification from GOCI Images using CNN Transfer Learning Models
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Title
- Sea Fog Classification from GOCI Images using CNN Transfer Learning Models
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Author(s)
- 전호군; Jonathan Edwin; 김승룡; 양찬수
- KIOST Author(s)
- Yang, Chan Su(양찬수)
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Alternative Author(s)
- 전호군; Jonathan Edwin; 김승룡; 양찬수
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Publication Year
- 2019-10-31
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Abstract
- 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.
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URI
- https://sciwatch.kiost.ac.kr/handle/2020.kiost/22075
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Bibliographic Citation
- ICSANE 2019, pp.87 - 90, 2019
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Publisher
- IEICE
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Type
- Conference
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Language
- English
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