基于卷积神经网络的全天空地基云图分类研究

Classification of All-sky Camera Data Based on Convolutional Neural Network

全天相机拍摄的全天空地基云图能够实时反映当地的云量信息,而云量是天文选址首先考虑的因素之一。因此,对全天空地基云图根据图像质量、应用背景等因素进行自动化分类,实现鲁棒性高、适应性强的自动化分类算法,为天文选址提供重要帮助。基于雪龙号全天相机数据对卷积神经网络模型进行训练,并使用丽江观测站全天相机数据进行测试,取得了较好的应用效果,实现了可迁移性高的全天空地基云图自动化分类方法。

The all-sky camera data is a common method for real-time cloud information monitoring,and cloud volume is one of the first considerations during astronomical site selection.Therefore,automatic classification of the all-sky foundation cloud image based on image quality,application background and other factors,and achieving an automatic classification algorithm with high robustness and adaptability,will provide important help for astronomical site selection.The paper uses the Xuelong all-sky camera data to train the convolutional neural network model,and uses the all-sky camera data of the Lijiang Observatory to test.It has achieved good application results and realized an automated classification method for all-sky camera data with high mobility.