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Proceedings Paper

A novel method using Gabor-based multiple feature and ensemble SVMs for ground-based cloud classification
Author(s): Ruitao Liu; Weidong Yang
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Paper Abstract

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.

Paper Details

Date Published: 2 December 2011
PDF: 8 pages
Proc. SPIE 8004, MIPPR 2011: Pattern Recognition and Computer Vision, 800418 (2 December 2011); doi: 10.1117/12.902758
Show Author Affiliations
Ruitao Liu, Huazhong Univ. of Science and Technology (China)
Weidong Yang, Huazhong Univ. of Science and Technology (China)

Published in SPIE Proceedings Vol. 8004:
MIPPR 2011: Pattern Recognition and Computer Vision
Jonathan Roberts; Jie Ma, Editor(s)

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