用U-net模型修复云层遮挡的海温图像，精度比传统方法高50%。
Deep Learning for Sea Surface Temperature Reconstruction under Cloud Occlusion
- 采用U-net卷积神经网络重建云遮区域的海温数据
- 相比经典插值法，均方根误差降低50%
- 适合遥感、海洋气候研究者使用
过去三十年中，卫星图像因云层遮挡导致海表温度（SST）重建问题备受关注。本文基于MODIS Aqua夜间L3数据，采用多种机器学习模型填补云遮区域。为解决此问题，我们使用一种卷积神经网络（U-net）模型，在保持无云区域观测值完整性的前提下，重建云覆盖区域的影像。实验表明，U-net在精度上显著优于现有的最优插值（OI）算法。表现最佳的模型相比现有填补方法，均方根误差降低50%。
原文摘要 · Abstract (English)
Sea Surface Temperature (SST) reconstructions from satellite images affected by cloud gaps have been extensively documented in the past three decades. Here we describe several Machine Learning models to fill the cloud-occluded areas starting from MODIS Aqua nighttime L3 images. To tackle this challenge, we employed a type of Convolutional Neural Network model (U-net) to reconstruct cloud-covered portions of satellite imagery while preserving the integrity of observed values in cloud-free areas. We demonstrate the outstanding precision of U-net with respect to available products done using OI interpolation algorithms. Our best-performing architecture show 50% lower root mean square errors over established gap-filling methods.
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