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

Randi FU; Guang SI; Wei JIN

doi:10.37188/OPE.20223008.0917

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

Modern Applied Optics | 更新时间：2022-04-26

- Cloud classification based on ensemble learning combining with deep neural network and FSVM
- Optics and Precision Engineering Vol. 30, Issue 8, Pages: 917-927(2022)
- 作者机构：
  
  宁波大学信息科学与工程学院，浙江宁波 315211
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20223008.0917
  CLC： TP751.1
- Received：15 March 2021，
  
  Revised：14 May 2021，
  
  Published：25 April 2022
- 稿件说明：
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符冉迪,司光,金炜.深度网络与FSVM集成学习的卫星云图云分类[J].光学精密工程,2022,30(08):917-927.

FU Randi,SI Guang,JIN Wei.Cloud classification based on ensemble learning combining with deep neural network and FSVM[J].Optics and Precision Engineering,2022,30(08):917-927.
符冉迪,司光,金炜.深度网络与FSVM集成学习的卫星云图云分类[J].光学精密工程,2022,30(08):917-927. DOI： 10.37188/OPE.20223008.0917.

FU Randi,SI Guang,JIN Wei.Cloud classification based on ensemble learning combining with deep neural network and FSVM[J].Optics and Precision Engineering,2022,30(08):917-927. DOI： 10.37188/OPE.20223008.0917.

摘要

准确的云分类模型对气象监测有重要的意义，传统机器学习云分类模型依赖手工特征提取，容易受噪声数据影响，模型泛化能力较差。深度网络分类模型能自动学习图像深度特征，但是对于图像边缘与细节分类效果不佳。本文针对上述问题进行研究。首先提取Himawari-8卫星云图光谱特征、纹理特征用以训练模糊支持向量机（Fuzzy Support Vector Machine，FSVM）模型；同时利用不同通道云图训练深度网络，学习云图深度特征；最后，根据不同模型特性，训练元分类器对各模型输出进行融合，设计了一种基于深度网络与FSVM集成学习的云分类方法，该方法综合不同模型优势，利用不同模型间的互补性提高云分类结果的鲁棒性和可信度。相比单独使用FSVM或深度网络的分类模型，本文集成学习方法在众多评价指标中有更好的表现，平均命中率、平均误报率和平均临界成功指数分别达到0.924 5、0.079 6、0.858 1；与其它云分类模型相比，本文方法也有更好的分类效果；在具体案例测试中也发现，该方法对于不同云类混合区有更高的识别精度，而且能更加准确的识别云团边缘及细节。本文模型能够满足云分类模型稳定可靠、高精度、泛化性能强的要求。

Abstract

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.

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