@article {RN177,
	title = {Deep Learning for Predicting Significant Wave Height From Synthetic Aperture Radar},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {PP},
	number = {99},
	year = {2020},
	pages = {1-9},
	type = {Journal Article},
	abstract = {The Sentinel-1 satellites equipped with synthetic aperture radars (SARs) provide near-global coverage of the world{\textquoteright}s oceans every six days. We curate a data set of collocations between SAR and altimeter satellites and investigate the use of deep learning to predict significant wave height from SAR. While previous models for predicting geophysical quantities from SAR rely heavily on feature-engineering, our approach learns directly from low-level image cross-spectra. Training on collocations from 2015 to 2017, we demonstrate on test data from 2018 that deep learning reduces the state-of-the-art root mean squared error by 50\%, from 0.6 to 0.3 m when compared to altimeter data. Furthermore, we isolate the contributions of different features to the model performance.},
	keywords = {Data models, Modulation, Satellites, Sea measurements, Sea surface, Synthetic aperture radar},
	doi = {10.1109/tgrs.2020.3003839},
	url = {https://app.dimensions.ai/details/publication/pub.1129457664},
	author = {Quach, Brandon and Glaser, Yannik and Stopa, Justin Edward and Mouche, Alexis Aurelien and Sadowski, Peter}
}
