Sea Fog Classification from GOCI Images using CNN Transfer Learning Models

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

Satellite Oceanography,Marine Safety & Security,Remote Sensing,위성해양학,해양 안전 및 보안,원격탐사

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