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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 8, issue 3
Atmos. Meas. Tech., 8, 1173–1182, 2015
https://doi.org/10.5194/amt-8-1173-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Meas. Tech., 8, 1173–1182, 2015
https://doi.org/10.5194/amt-8-1173-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 10 Mar 2015

Research article | 10 Mar 2015

Block-based cloud classification with statistical features and distribution of local texture features

H.-Y. Cheng1 and C.-C. Yu2 H.-Y. Cheng and C.-C. Yu
  • 1Department of Computer Science and Information Engineering, National Central University, 300 Jongda Rd., Zhongli Dist., Taoyuan City 32001, Taiwan
  • 2Department of Computer Science and Information Engineering, Vanung University, 1 Wanneng Rd., Zhongli Dist., Taoyuan City 32061, Taiwan

Abstract. This work performs cloud classification on all-sky images. To deal with mixed cloud types in one image, we propose performing block division and block-based classification. In addition to classical statistical texture features, the proposed method incorporates local binary pattern, which extracts local texture features in the feature vector. The combined feature can effectively preserve global information as well as more discriminating local texture features of different cloud types. The experimental results have shown that applying the combined feature results in higher classification accuracy compared to using classical statistical texture features. In our experiments, it is also validated that using block-based classification outperforms classification on the entire images. Moreover, we report the classification accuracy using different classifiers including the k-nearest neighbor classifier, Bayesian classifier, and support vector machine.

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Short summary
This work performs cloud classification on all-sky images. To deal with mixed cloud types, we propose performing block-based classification. The proposed method combines local texture features with classical statistical texture features. The experimental results have shown that applying the combined feature results in higher classification accuracy. It is also validated that using block-based classification outperforms classification on the entire images.
This work performs cloud classification on all-sky images. To deal with mixed cloud types, we...
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