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Min Wang, Shudao Zhou, Zhong Yang, and Zhanhua Liu


Conventional classification methods are based on artificial experience to extract features, and each link is independent, which is a kind of “shallow learning.” As a result, the scope of the cloud category applied by this method is limited. In this paper, we propose a new convolutional neural network (CNN) with deep learning ability, called CloudA, for the ground-based cloud image recognition method. We use the Singapore Whole-Sky Imaging Categories (SWIMCAT) sample library and total-sky sample library to train and test CloudA. In particular, we visualize the cloud features captured by CloudA using the TensorBoard visualization method, and these features can help us to understand the process of ground-based cloud classification. We compare this method with other commonly used methods to explore the feasibility of using CloudA to classify ground-based cloud images, and the evaluation of a large number of experiments show that the average accuracy of this method is nearly 98.63% for ground-based cloud classification.

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Min Wang, Shudao Zhou, Zhanhua Liu, and Yangchun Zhang


The reflection of colors and surfaces of common targets lead to errors in the measurement of visibility by the image method. This study aims to investigate the problem of inaccurate visibility detection. Through analysis of the error of visibility measurement caused by the reflection of the blackboard surface of an artificial target, the design method of improving the structure of the target board is proposed, so as to improve the accuracy of atmospheric visibility measurement by the image method. The experimental results show that the new target board designed by this method can greatly improve the measurement accuracy of the intrinsic apparent brightness ratio, which can increase 18.4% in the fairing environment and closer to −1 in the side light environment. Therefore, when the side light is selected for the image method visibility measurement, more accurate visibility results can be obtained.

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