| アイテムタイプ |
学術雑誌論文 = Journal Article(1) |
| 公開日 |
2023-12-04 |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
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資源タイプ |
journal article |
| タイトル |
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タイトル |
Earth Observation Mission of a 6U CubeSat with a 5-Meter Resolution for Wildfire Image Classification Using Convolution Neural Network Approach |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| 著者 |
Azami, Muhammad Hasif bin
Orger, Necmi Cihan
シュルツ, ビクトル ユーゴ
Oshiro, Takashi
趙, 孟佑
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
The KITSUNE satellite is a 6-unit CubeSat platform with the main mission of 5-m-class Earth observation in low Earth orbit (LEO), and the payload is developed with a 31.4 MP commercial off-the-shelf sensor, customized optics, and a camera controller board. Even though the payload is designed for Earth observation and to capture man-made patterns on the ground as the main mission, a secondary mission is planned for the classification of wildfire images by the convolution neural network (CNN) approach. Therefore, KITSUNE will be the first CubeSat to employ CNN to classify wildfire images in LEO. In this study, a deep-learning approach is utilized onboard the satellite in order to reduce the downlink data by pre-processing instead of the traditional method of performing the image processing at the ground station. The pre-trained CNN models generated in Colab are saved in RPi CM3+, in which, an uplink command will execute the image classification algorithm and append the results on the captured image data. The on-ground testing indicated that it could achieve an overall accuracy of 98% and an F1 score of a 97% success rate in classifying the wildfire events running on the satellite system using the MiniVGGNet network. Meanwhile, the LeNet and ShallowNet models were also compared and implemented on the CubeSat with 95% and 92% F1 scores, respectively. Overall, this study demonstrated the capability of small satellites to perform CNN onboard in orbit. Finally, the KITSUNE satellite is deployed from ISS on March 2022. |
|
言語 |
en |
| 書誌情報 |
en : Remote Sensing
巻 14,
号 8,
p. 1874,
発行日 2022-04-13
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| 出版社 |
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出版者 |
MDPI |
| DOI |
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関連タイプ |
isIdenticalTo |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.3390/rs14081874 |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
2072-4292 |
| 著作権関連情報 |
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権利情報Resource |
https://creativecommons.org/licenses/by/4.0/ |
|
権利情報 |
Copyright (c) 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. |
| 出版タイプ |
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出版タイプ |
VoR |
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出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| 査読の有無 |
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値 |
yes |
| 研究者情報 |
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|
URL |
https://hyokadb02.jimu.kyutech.ac.jp/html/100001782_ja.html |
LaSEINE-2022_002.pdf (1.4 MB)
