Cloud classification based on ensemble learning combining with deep neural network and FSVM

Accurate cloud classification is of great significance for meteorological monitoring. Traditional machine learning models rely on hand-craft featurs, which is sensitive to noise data and the generalization ability is also poor. Deep neural network can automatically learn the depth features of image, but it is not good at image edge and detail classification, this paper studies on the basis of the above problems. First, the spectral features and texture features are extracted from himawari-8 satellite images to train fuzzy support vector machine (FSVM) model. At the same time, different channels of cloud images are selected to train deep neural network to learn the depth features for cloud classification. Finally, according to the characteristics of different models, the output of the two models is fused by ensemble learning to improve the classification accuracy. This article designs a cloud classification model based on ensemble learning which fuses deep neural network and FSVM. It combines the advantages of different models and makes use of the complementarity between different models to improve the robustness and reliability of the model.The experimental results show that: compared with model which uses a single model alone, the ensemble learning method proposed in this article has better performance in different evaluation indicators, The average POD, FAR and CSI were 0.9245, 0.0796 and 0.8581 respectively; this method also has better recognition effect when compared with other cloud classification models; in specific cases, it is found that this method has higher recognition accuracy in clouds mixed regions, and it can identify cloud edge and cloud details more accurately.This model can satisfy the requirements of stability, reliability, high precision and strong generalization performance of cloud classification model. © 2022, Science Press. All right reserved.