Copernicus Gesellschaft mbH;Katlenburg-Lindau: Copernicus Publications
摘要:
摘要： 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. 出版者： Katlenburg-Lindau: Copernicus Publications 出版日期： 2015-03-10 出處： Atmospheric measurement techniques, 2015-03, Vol.8 (3), p.1173-1182 資源來源： Publicly Available Content Database 版權： COPYRIGHT 2015 Copernicus GmbH 版權： Copyright Copernicus GmbH 2015 版權： 2015. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 識別號： ISSN: 1867-8548 識別號： ISSN: 1867-1381 識別號： EISSN: 1867-8548 識別號： EISSN: 1867-1381 識別號： DOI: 10.5194/amt-8-1173-2015
