Abstract: The classification of satellite cloud images has always been one of the research hotspots in the field of meteorology.But there are some problems,such as the same cloud type has different spectral features,different cloud types have the same spectral features,and mainly use the spectral features and ignore spatial features.To solve the above problems,this paper proposes a cloud classification method of satellite cloud image based on CNN-LSTM,which makes full use of spectral information and spatial information to improve the accuracy of cloud classification.Firstly,the spectral features are screened based on the physical characteristics of the cloud,and the square neighborhood of the point cloud is used as the spatial information.Then,the convolutional neural network(CNN) is used to automatically extract the spatial features,which solves the problem of difficult classification with spectral feature alone.Finally,on this basis,combined with the spatial local difference features extracted by the long short-term memory(LSTM) network,it provides multi-view features for the classification of satellite cloud images,and solves the problem of misjudgment caused by the similarity of cloud spatial structure.Experimental results show that the overall classification accuracy of the proposed method for satellite cloud images reaches 93.4%,which is 2.7% higher than that of the single CNN method.

Key words: Cloud classification, CNN-LSTM, Convolution neural network, Long short-term memory network, Spectral features