Cloud-Free Sea-Surface-Temperature Image Reconstruction From Anomaly Inpainting Network
Cloud-Free Sea-Surface-Temperature Image Reconstruction From Anomaly Inpainting Network
复制标题
从异常修复网络重建无云海面温度图像
DOI：
10.1109/tgrs.2021.3111649
复制
发表时间：
2021
影响因子：
8.2
通讯作者：
Masaaki Iiyama
中科院分区：
文献类型：
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作者：
Nobuyuki Hirahara;Motoharu Sonogashira;Masaaki Iiyama
卫星获取的海面温度(SST)图像包含噪声和由于云层覆盖而丢失的SST。提出了一种基于深度学习的图像修复方法，用于重建去噪的无云海温图像。在去噪方面，我们使用数据同化图像来训练重建网络，同时考虑了SSTS的物理正确性。为了提高重建的稳定性，我们引入了异常修复网络，它不是直接补全缺失的表面温度，而是估计未观测到的表面温度与平均表面温度之间的差值。SST在几天内波动不大；因此，我们可以使用最近的平均SST作为SST的粗略估计，并可以假设SST差值将在特定范围内。我们用卫星SST图像和情景SST数据对我们的方法进行了评估。结果表明，基于异常网络的海温图像修复方法在定性和定量上都优于传统的海温图像修复方法。
Sea-surface temperature (SST) images obtained by satellites contain noise and missing SSTs due to cloud covers. We propose a method for reconstructing denoised cloud-free SST images via deep-learning-based image inpainting. For denoizing, we use data-assimilation images to train a reconstruction network by considering the physical correctness of SSTs. For reconstruction stability, we introduceanomaly inpainting network, which does not directly complete missing SSTs but estimates the difference between the unobserved SSTs and the average SSTs. SSTs do not fluctuate much over a few days; thus, we can use recent average SSTs as a rough estimation of SSTs and can assume that the SST difference will be within a specific range. We conducted experiments to evaluate our method with satellite SST images andin situSST data. The results indicate that our method with anomaly inpainting network qualitatively and quantitatively outperformed conventional SST image inpainting methods.
从红外图像中反演海面温度：系统误差的起源和形式
影响因子：
8.9
作者：
C. Merchant;L. Horrocks;J. Eyre;A. O'carroll
通讯作者：
A. O'carroll
深度（呃）学习。
DOI：
10.1523/jneurosci.0153-18.2018
发表时间：
2018
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Srinivasan,Shyam;Greenspan,RalphJ;Stevens,CharlesF;Grover,Dhruv
通讯作者：
Grover,Dhruv