Multimodal Deep Learning for Heterogeneous GNSS-R Data Fusion and Ocean Wind Speed Retrieval

Multimodal Deep Learning for Heterogeneous GNSS-R Data Fusion and Ocean Wind Speed Retrieval

The comprehensiveness of the raw input data and the effectiveness of feature engineering are two key factors affecting the performance of machine learning. To improve the data comprehensiveness for Global Navigation Satellite System Reflectometry (GNSS-R) ocean wind speed retrieval, this article int...

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Bibliographic Details
Main Authors: Xiaohan Chu, Jie He, Hongqing Song, Yue Qi, Yueqiang Sun, Weihua Bai, Wei Li, Qiwu Wu
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Delay–Doppler map (DDM)
GNSS-reflectometry (GNSS-R)
multimodal deep learning
ocean surface wind retrieval
Online Access:https://ieeexplore.ieee.org/document/9165055/

Internet

https://ieeexplore.ieee.org/document/9165055/

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