FA-RDN: A Hybrid Neural Network on GNSS-R Sea Surface Wind Speed Retrieval

FA-RDN: A Hybrid Neural Network on GNSS-R Sea Surface Wind Speed Retrieval

Based on deep learning, this paper proposes a new hybrid neural network model, a recurrent deep neural network using a feature attention mechanism (FA-RDN) for GNSS-R global sea surface wind speed retrieval. FA-RDN can process data from the Cyclone Global Navigation Satellite System (CYGNSS) satelli...

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Bibliographic Details
Main Authors: Xiaoxu Liu, Weihua Bai, Junming Xia, Feixiong Huang, Cong Yin, Yueqiang Sun, Qifei Du, Xiangguang Meng, Congliang Liu, Peng Hu, Guangyuan Tan
Format: Article
Language:English
Published: MDPI AG 2021-11-01
Series:Remote Sensing
Subjects:
GNSS-R
sea surface wind speed retrieval
deep learning
long-short term memory (LSTM) neural network
attention mechanism
Online Access:https://www.mdpi.com/2072-4292/13/23/4820

Internet

https://www.mdpi.com/2072-4292/13/23/4820

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