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Article

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

by 1,2,3,4,5, 1,2,3,4,5,*, 1,3,4,5, 1,3,4,5, 1,3,4,5, 1,2,3,4,5, 1,3,4,5, 1,3,4,5, 1,3,4,5, 1,3,4,5 and 1,3,4,5
1
National Space Science Center, Chinese Academy of Sciences (NSSC/CAS), Beijing 100190, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Beijing Key Laboratory of Space Environment Exploration, Beijing 100190, China
4
Key Laboratory of Science and Technology on Space Environment Situational Awareness, CAS, Beijing 100190, China
5
Joint Laboratory on Occultations for Atmosphere and Climate (JLOAC), NSSC/CAS and University of Graz, Beijing 100190, China
*
Author to whom correspondence should be addressed.
Academic Editor: Ali Khenchaf
Remote Sens. 2021, 13(23), 4820; https://doi.org/10.3390/rs13234820
Received: 5 November 2021 / Revised: 24 November 2021 / Accepted: 24 November 2021 / Published: 27 November 2021
(This article belongs to the Section Ocean Remote Sensing)
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) satellite mission, including characteristics of the signal, spatio-temporal, geometry, and instrument. FA-RDN can receive data extended in temporal dimension and mine the temporal correlation information of features through the long-short term memory (LSTM) neural network layer. A feature attention mechanism is also added to improve the model’s computational efficiency. To evaluate the model performance, we designed comparison and validation experiments for the retrieval accuracy, enhancement effect, and stability of FA-RDN by comparing the evaluation criteria results. The results show that the wind speed retrieval root mean square error (RMSE) of the FA-RDN model can reach 1.45 m/s, 10.38%, 6.58%, 13.28%, 17.89%, 20.26%, and 23.14% higher than that of Backpropagation Neural Network (BPNN), Recurrent Neural Network (RNN), Artificial Neural Network (ANN), Random Forests (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR), respectively, confirming the feasibility and effectiveness of the designed method. At the same time, the designed model has better stability and applicability, serving as a new research idea of data mining and feature selection, as well as a reference model for GNSS-R-based sea surface wind speed retrieval. View Full-Text
Keywords: GNSS-R; sea surface wind speed retrieval; deep learning; long-short term memory (LSTM) neural network; attention mechanism GNSS-R; sea surface wind speed retrieval; deep learning; long-short term memory (LSTM) neural network; attention mechanism
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MDPI and ACS Style

Liu, X.; Bai, W.; Xia, J.; Huang, F.; Yin, C.; Sun, Y.; Du, Q.; Meng, X.; Liu, C.; Hu, P.; Tan, G. FA-RDN: A Hybrid Neural Network on GNSS-R Sea Surface Wind Speed Retrieval. Remote Sens. 2021, 13, 4820. https://doi.org/10.3390/rs13234820

AMA Style

Liu X, Bai W, Xia J, Huang F, Yin C, Sun Y, Du Q, Meng X, Liu C, Hu P, Tan G. FA-RDN: A Hybrid Neural Network on GNSS-R Sea Surface Wind Speed Retrieval. Remote Sensing. 2021; 13(23):4820. https://doi.org/10.3390/rs13234820

Chicago/Turabian Style

Liu, Xiaoxu, Weihua Bai, Junming Xia, Feixiong Huang, Cong Yin, Yueqiang Sun, Qifei Du, Xiangguang Meng, Congliang Liu, Peng Hu, and Guangyuan Tan. 2021. "FA-RDN: A Hybrid Neural Network on GNSS-R Sea Surface Wind Speed Retrieval" Remote Sensing 13, no. 23: 4820. https://doi.org/10.3390/rs13234820

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