A MACHINE LEARNING FRAMEWORK FOR REAL DATA GNSS-R WIND SPEED RETRIEVAL

被引：0

作者：

Liu, Yunxiang ^{[1
]}

Wang, Jun ^{[1
]}

Collett, Ian ^{[1
]}

Morton, Y. Jade ^{[1
]}

机构：

[1] Univ Colorado, Colorado Ctr Astrodynam Res, Smead Aerosp Engn Sci, Boulder, CO 80309 USA

来源：

2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019) | 2019年

关键词：

CYGNSS; GNSS-R; wind speed retrieval; machine learning; feature engineering; multi-hidden layer neural network;

D O I：

10.1109/igarss.2019.8899792

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

In this paper, we propose a machine learning framework to conduct GNSS-R wind speed retrieval. While the conventional method tries to retrieve wind speed using a single scalar value, the proposed framework is capable of incorporating and employing more features such as DDM and incidence angle. The results show that the proposed framework outperforms the conventional retrieval method with a notable margin.

引用

页码：8707 / 8710

页数：4

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