论文标题
使用深度学习方法从卫星云图像中产生云运动场
Generating the Cloud Motion Winds Field from Satellite Cloud Imagery Using Deep Learning Approach
论文作者
论文摘要
云运动风（CMW）通常通过在顺序地静止卫星红外云图像中跟踪特征来常规得出。在本文中，我们根据数据驱动的深度学习方法探索云运动算法，并且与传统的手工艺特征跟踪和相关匹配算法不同，我们使用深度学习模型自动学习运动特征表示并直接输出云运动风的场。此外，我们提出了一种新型的大规模云运动风格数据集（CMWD），用于训练深度学习模型。我们还尝试使用单个云图像来预测固定区域中的云运动风场，这是不可能使用传统算法实现的。实验结果表明，我们的算法可以有效地预测云运动场，即使是单个云成像作为输入。
Cloud motion winds (CMW) are routinely derived by tracking features in sequential geostationary satellite infrared cloud imagery. In this paper, we explore the cloud motion winds algorithm based on data-driven deep learning approach, and different from conventional hand-craft feature tracking and correlation matching algorithms, we use deep learning model to automatically learn the motion feature representations and directly output the field of cloud motion winds. In addition, we propose a novel large-scale cloud motion winds dataset (CMWD) for training deep learning models. We also try to use a single cloud imagery to predict the cloud motion winds field in a fixed region, which is impossible to achieve using traditional algorithms. The experimental results demonstrate that our algorithm can predict the cloud motion winds field efficiently, and even with a single cloud imagery as input.
