DMANet_KF: Tropical Cyclone Intensity Estimation Based on Deep Learning and Kalman Filter From Multispectral Infrared Images-Reference-Cited by-同舟云学术

DMANet_KF: Tropical Cyclone Intensity Estimation Based on Deep Learning and Kalman Filter From Multispectral Infrared Images

Published:2023 Issue: Volume:16 Page:4469-4483
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Jiang Wenjun¹^ORCID,Hu Gang¹^ORCID,Wu Tiantian¹,Liu Lingbo²,Kim Bubryur³,Xiao Yiqing¹,Duan Zhongdong¹

Affiliation:

1. School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen, China

2. Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong

3. Department of Robot and Smart System Engineering, Kyungpook National University, Daegu, South Korea

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Shenzhen Science and Technology Innovation Commission

Shenzhen Key Laboratory Launching Project

Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Link

Reference56 articles.

1. Eyed tropical cyclone intensity objective estimation model based on infrared satellite image and relevance vector machine;dai;J Remote Sens,2018

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2. A Deep Learning Based Novel Approach For Identifying Cyclone with XVNET Framework;2024 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI);2024-04-17