合成孔径雷达
有效波高
遥感
高度计
深度学习
计算机科学
均方误差
特征工程
雷达高度计
数据集
人工智能
特征(语言学)
雷达成像
地质学
雷达
风浪
数学
电信
海洋学
语言学
统计
哲学
作者
Brandon Quach,Yannik Glaser,Justin E. Stopa,Alexis Mouche,Peter Sadowski
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2021-03-01
卷期号:59 (3): 1859-1867
被引量:30
标识
DOI:10.1109/tgrs.2020.3003839
摘要
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.
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