A Novel Method Using Gabor-based Multiple Feature and Ensemble SVMs For Ground-based Cloud Classification
Cloud recognition is the base of weather forecast and the recognition of cloud types is challenging because the texture the clouds is extremely variable under different atmospheric conditions. In this paper, we propose a novel method for ground-based cloud classification. Firstly, the interest operator feature (IO) and the sorted spectral histogram (SSH) feature are generated from Gabor-filtered images and then they are selected by using the principal component analysis (PCA), which can reduce the feature’s dimension. Secondly the new training set is selected using the supervised clustering technology. Finally we send the two features to the multi-class SVM classifier, and a voting algorithm is used to determine the category of each cloud. In practice, we find no single feature is best suited for recognizing all these classes. The result shows that this method has higher classfication accuracy and lower space complexity than the other methods.
Ground-based cloud classification Gabor filter sorted spectral histogram support vector machine
Ruitao Liu Weidong Yang
The National Laboratory for Multispectral Information Processing Technologies, Institute for Pattern The National Laboratory for Multispectral Information Processing Technologies, Institute for Pattern
国际会议
桂林
英文
1-8
2011-11-01（万方平台首次上网日期，不代表论文的发表时间）