@inproceedings{3ab1e8ca22434a029a3e524e650a4bd8,
title = "DDM-Former: Global Ocean Wind Speed Retrieval with Transformer Networks",
abstract = "As a novel remote sensing technique, GNSS reflectometry (GNSS-R) opens a new era of retrieving Earth surface parameters. Several studies employ the combination of deep learning and GNSS-R observable delay-Doppler maps (DDMs) to generate ocean wind speed estimation. Unlike these methods that often use convolutional neural networks (CNNs) with inductive bias, we proposed a Transformer-based model, named DDM-Former, to exploit fine-grained delay-Doppler correlation independently. Our model is evaluated on the Cyclone GNSS (CYGNSS) version 3.0 dataset and shown to outperform the other retrieval methods.",
keywords = "Cyclone GNSS, deep learning, GNSS reflectometry, ocean wind speed, Transformer network",
author = "Daixin Zhao and Konrad Heidler and Milad Asgarimehr and Caroline Arnold and Tianqi Xiao and Jens Wickert and Zhu, {Xiao Xiang} and Lichao Mou",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 ; Conference date: 16-07-2023 Through 21-07-2023",
year = "2023",
doi = "10.1109/IGARSS52108.2023.10281607",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1182--1185",
booktitle = "IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings",
}