Cloud Detection And Dynamic Change Analysis Based On Deep Learning

The satellite cloud image contains rich meteorological information,and the changes of the cloud cluster are closely related to the weather conditions and natural wonders(Jiuzhaigou Cloud sea,Lushan Cloud,etc.).Hence,the cloud cluster detection and movement prediction are crucial for the development of weather forecast,disaster prediction and even tourism.Recently,how to extract cloud information quickly and accurately from remote sensing images has become a key issue for many researchers.Firstly,this study proposes a cloud detection method based on the convolutional neural network,by which the cloud clusters and clear sky underlying surface are classified in the remote sensing images.Then,a cloud movement analysis and prediction method is proposed based on the singular value decomposition algorithm and the deep neural network,to analyze the movement trajectories and predict the dynamic trend of the cloud cluster in a short period.The main research contents are as follows:(1)This paper studies the network structure of convolutional neural network(CNN)in deep learning,expounds the concepts and principles of convolutional layers,activation functions and loss functions in the network,and summarizes the advantages,disadvantages and applicabilities of convolutional neural networks.Based on this,the convolutional neural network(CNN)is utilized to classify cloud clusters and clear sky underlying surface in the remote sensing image,and the influence of the size and number of convolution kernels on the operation result are analyzed in the network operation.After determining the best network architecture for cloud detection,ten evaluation indicators such as confusion matrix and ROC curve are selected to evaluate the accuracy of convolutional neural network.Finally,the proposed cloud detection method is compared with the method based on support vector machine(SVM).The detection results of the proposed method are better than that based on the support vector machine.(3)The singular value decomposition(SVD)is applied to the remote sensing image,where the appropriate statistic is selected as the recognition factor.Combined with deep neural network(DNN),the dynamic changes of cloud clusters in remote sensing images are analyzed,and the trajectories of the cloud clusters are predicted in a short period.Then,cosine similarity and structural similarity index measure(SSIM)were chosen as the performance metrics and the results of cloud dynamic change analysis based on DNN and the method based on linear regression(LR)are compared.Experiment results show that the cloud prediction method proposed in this paper can obtain better performance.