With the development of science and technology,the research on ground-based clouds has become more and more in-depth,and the research on ground-based clouds is important for weather forecasting,water resources management,agricultural pro-duction and other fields.Traditional ground-based cloud classification methods have problems such as large data requirements and slow operation rates.The study constructs a ground-based cloud classifier that combines a two-channel Convolutional Neural Net-works(CNN)algorithm with compression-awareness in order to solve these problems.Firstly,a dual-channel CNN algorithm is ob-tained by improving the CNN,and then it is fused with compressive sensing to obtain a ground-based cloud artificial intelligence clas-sifier;finally,a pair of different methods is conducted to verify the classification ability of the constructed ground-based cloud classi-fier for ground-based clouds.The results show that the average recognition accuracy of the ground-based cloud classifier is 73.95％,45.39％,92.61％and 43.82％under the observation premise of normal light,low light,horizontal angle and pitch angle,which are higher than the control algorithm.This indicates that this ground-based cloud classifier has high accuracy and robustness.
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