Name
Abstract | ||
|---|---|---|
The Sentinel-1 satellites equipped with synthetic aperture radars (SARs) provide near-global coverage of the world'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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TGRS.2020.3003839 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | DocType | Volume |
CWAVE,deep learning,machine learning,neural networks,Sentinel-1,significant wave height,synthetic aperture radar (SAR) | Journal | 59 |
Issue | ISSN | Citations |
3 | 0196-2892 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Brandon Quach | 1 | 0 | 0.34 |
| Yannik Glaser | 2 | 0 | 0.34 |
| Justin Stopa | 3 | 1 | 2.71 |
| Alexis Mouche | 4 | 85 | 21.13 |
| Peter J. Sadowski | 5 | 176 | 18.03 |