改进的主成分分析网络极光图像分类方法

doi:10.3969/j.issn.1001-2400.2017.01.015

Abstract

Abstract:

The mysterious aurora is changeable, and the different forms of the aurora represent various physical processes which often affect our lives. So, it is of significant scientific value to classify the aurora images for the study of space physics. Based on the PCANet, a simple deep learning model, we develop an improved PCANet algorithm for aurora images classification. Firstly, the map of aurora images are extracted by the improved PCANet. Then the support vector machine is used to classify the feature of aurora images. Experimental results with the dataset obtained from the All-sky Imager at the Chinese Arctic Yellow River Station demonstrate that the scheme can obtain higher accuracy in aurora image classification than the PCANet.

Key words: dayside aurora, deep learning, principle component analysis, 2DPCA, PCANet

HAN Bing;JIA Zhonghua;GAO Xinbo. Improved PCANet for aurora images classification[J].Journal of Xidian University, 2017, 44(1): 83-88.

References

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Improved PCANet for aurora images classification

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