一种特征显著性编码的极光图像分类方法

doi:10.3969/j.issn.1001-2400.2013.06.030

Abstract

Abstract:

The change of the form of the aurora reveals the atmospheric activities and the degree of the influence of the sun on the earth. The research on aurora image classification is an effective way to study the aurora phenomenon. In this paper, an image classification method for auroras is developed. According to the characteristics of aurora images, the SIFT features of aurora image are extracted. Then the clustering centers of all SIFT features are obtained by the fuzzy C-means clustering method. Further, the weights of those clustering centers are calculated by the Salient Coding method and they are regarded as the final features for final aurora classification. Finally, the support vector machine is used to classify the 3200 aurora images. Experimental results show that the proposed method has a good performance not only on arc shape aurora images but also on complicated crown shape aurora images.

Key words: dayside aurora, scale-invariant feature transform features, fuzzy C-means, salient coding, image classification

CLC Number:

TN911.73

HAN Bing;QIU Wenliang. Aurora images classification via features salient coding[J].J4, 2013, 40(6): 180-186.

References

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Aurora images classification via features salient coding

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