Fa Tao, Xie Wanyi, Wang Yiren, Xia Yingwei
Appl Opt. 2019 Jul 10;58(20):5516-5524. doi: 10.1364/AO.58.005516.
This study develops a novel automatic all-sky imaging system, namely, an all-sky camera (ASC) system, for cloud cover assessment. The proposed system does not require conventional solar occulting devices and can capture complete hemispheric sky images. Cloud detection is performed innovatively using a convolutional neural network model (i.e., the optimized U-Net model). Experiments demonstrate that the optimized U-Net model can effectively detect clouds from sky images. In terms of cloud cover, the estimation results of the ASC system exhibit a high correlation with those obtained via manual observation, thereby indicating the applicability of the ASC system in ground-based cloud observation and analysis.
本研究开发了一种用于云量评估的新型自动全天空成像系统，即全天空相机（ASC）系统。所提出的系统不需要传统的太阳遮挡装置，并且可以捕获完整的半球天空图像。利用卷积神经网络模型（即优化的U-Net模型）创新性地进行云检测。实验表明，优化的U-Net模型能够有效地从天空图像中检测云。在云量方面，ASC系统的估计结果与通过人工观测获得的结果具有高度相关性，从而表明ASC系统在地面云观测和分析中的适用性。
