Global Ocean Significant Wave Height Retrieval From Spaceborne GNSS-R Data Using a CNN-Transformer Network

This study uses a deep learning method to retrieve significant wave height (SWH) from global navigation satellite system reflectometry data. To combine the advantages of convolutional neural networks and transformers, a hybrid network called CTWvHtNet is proposed. The model takes bistatic radar cros...

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Bibliografische Detailangaben

Hauptverfasser:	Jiacheng Zheng, Zili Li
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	IEEE 2026-01-01
Schriftenreihe:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Schlagworte:	Cyclone global navigation satellite system (CYGNSS) global navigation satellite system reflectometry (GNSS-R) significant wave height (SWH) convolutional neural networks (CNN) transformers
Online-Zugang:	https://ieeexplore.ieee.org/document/11505776/
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