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A study on the DAM-EfficientNet hail rapid identification algorithm based on FY-4A_AGRI

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成果类型:
期刊论文
作者:
Liu, Renfeng;Dai, Haonan;Chen, Yingying;Zhu, Hongxing;Wu, Daiheng;...
通讯作者:
Chen, YY
作者机构:
[Dai, Haonan; Wu, Daiheng; Zhu, Hongxing; Liu, Renfeng] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.
[Li, Dejun; Chen, Yingying] Hubei Meteorol Serv Ctr, Wuhan 430205, Peoples R China.
[Li, Hao] Guizhou Prov Off Artificial Weather Modificat, Guiyang 550081, Peoples R China.
[Zhou, Cheng] South Cent Minzu Univ, Hubei Key Lab Intelligent Wireless Commun, Wuhan 430074, Peoples R China.
通讯机构:
[Chen, YY ] H
Hubei Meteorol Serv Ctr, Wuhan 430205, Peoples R China.
语种:
英文
关键词:
DAM-EfficientNet;Deep learning;FY-4A;Hail
期刊:
Scientific Reports
ISSN:
2045-2322
年:
2024
卷:
14
期:
1
页码:
3505
机构署名:
本校为第一机构
院系归属:
数学与计算机学院
摘要:
AbstractHail, a highly destructive weather phenomenon, necessitates critical identification and forecasting for the protection of human lives and properties. The identification and forecasting of hail are vital for ensuring human safety and safeguarding assets. This research proposes a deep learning algorithm named Dual Attention Module EfficientNet (DAM-EfficientNet), based on EfficientNet, for detecting hail weather conditions. DAM-EfficientNet was evaluated using FY-4A satellite imagery and real hail fall records, achieving an accuracy of 98...

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