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

H. Y. Cheng, C. C. Yu

研究成果: 雜誌貢獻期刊論文同行評審

30 引文 斯高帕斯(Scopus)

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.

原文???core.languages.en_GB???
頁(從 - 到)1173-1182
頁數10
期刊Atmospheric Measurement Techniques
8
發行號3
DOIs
出版狀態已出版 - 10 3月 2015

指紋

深入研究「Block-based cloud classification with statistical features and distribution of local texture features」主題。共同形成了獨特的指紋。

引用此