| Item type |
デフォルトアイテムタイプ（フル）(1) |
| 公開日 |
2021-09-02 |
| タイトル |
|
|
タイトル |
Domain knowledge integration into deep learning for typhoon intensity classifcation |
|
言語 |
en |
| 作成者 |
Higa, Maiki
Tanahara, Shinya
Adachi, Yoshitaka
Ishiki, Natsumi
Nakama, Shin
Yamada, Hiroyuki
Ito, Kosuke
Kitamoto, Asanobu
Miyata, Ryota
|
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 権利情報 |
|
|
言語 |
ja |
|
権利情報 |
© The Author(s) 2021 |
| 権利情報 |
|
|
言語 |
en |
|
権利情報Resource |
http://creativecommons.org/licenses/by/4.0/ |
|
権利情報 |
http://creativecommons.org/licenses/by/4.0/ |
| 内容記述 |
|
|
内容記述タイプ |
Other |
|
内容記述 |
In this report, we propose a deep learning technique for high-accuracy estimation of the intensity class of a typhoon from a single satellite image, by incorporating meteorological domain knowledge. By using the Visual Geometric Group’s model, VGG-16, with images preprocessed with fisheye distortion, which enhances a typhoon’s eye, eyewall, and cloud distribution, we achieved much higher classification accuracy than that of a previous study, even with sequential-split validation. Through comparison of t-distributed stochastic neighbor embedding (t-SNE) plots for the feature maps of VGG with the original satellite images, we also verified that the fisheye preprocessing facilitated cluster formation, suggesting that our model could successfully extract image features related to the typhoon intensity class. Moreover, gradient-weighted class activation mapping (Grad-CAM) was applied to highlight the eye and the cloud distributions surrounding the eye, which are important regions for intensity classification; the results suggest that our model qualitatively gained a viewpoint similar to that of domain experts. A series of analyses revealed that the data-driven approach using only deep learning has limitations, and the integration of domain knowledge could bring new breakthroughs. |
| 内容記述 |
|
|
内容記述タイプ |
Other |
|
内容記述 |
論文 |
| 出版者 |
|
|
出版者 |
Nature Research |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| 識別子 |
|
|
識別子 |
http://hdl.handle.net/20.500.12000/48845 |
|
識別子タイプ |
HDL |
| 関連情報 |
|
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1038/s41598-021-92286-w |
| 関連情報 |
|
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1038/s41598-021-92286-w |
| 収録物識別子 |
|
|
収録物識別子タイプ |
ISSN |
|
収録物識別子 |
2045-2322 |
| 収録物名 |
|
|
収録物名 |
Scientific Reports |
|
言語 |
en |
| 書誌情報 |
巻 11,
発行日 2021-06-21
|
s41598-021-92286-w.pdf
