Cloud recognition is the base of weather forecast and the recognition of cloud types is challenging because the texture of
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.
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