dc.contributor.authorZhuo, Wen
dc.contributor.authorCao, Zhiguo
dc.contributor.authorXiao, Yang
dc.date.accessioned2014-02-17T03:41:06Z
dc.date.available2014-02-17T03:41:06Z
dc.date.copyright2014en_US
dc.date.issued2014
dc.identifier.citationZhuo, W., Cao, Z., & Xiao, Y. (2014). Cloud Classification of Ground-Based Images Using Texture–Structure Features. Journal of Atmospheric and Oceanic Technology, 31(1), 79-92.en_US
dc.identifier.urihttp://hdl.handle.net/10220/18802
dc.description.abstractCloud classification of ground-based images is a challenging task. Recent research has focused on extracting discriminative image features, which are mainly divided into two categories: 1) choosing appropriate texture features and 2) constructing structure features. However, simply using texture or structure features separately may not produce a high performance for cloud classification. In this paper, an algorithm is proposed that can capture both texture and structure information from a color sky image. The algorithm comprises three main stages. First, a preprocessing color census transform (CCT) is applied. The CCT contains two steps: converting red, green, and blue (RGB) values to opponent color space and applying census transform to each component. The CCT can capture texture and local structure information. Second, a novel automatic block assignment method is proposed that can capture global rough structure information. A histogram and image statistics are computed in every block and are concatenated to form a feature vector. Third, the feature vector is fed into a trained support vector machine (SVM) classifier to obtain the cloud type. The results show that this approach outperforms other existing cloud classification methods. In addition, several different color spaces were tested and the results show that the opponent color space is most suitable for cloud classification. Another comparison experiment on classifiers shows that the SVM classifier is more accurate than the k–nearest neighbor (k-NN) and neural networks classifiers.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesJournal of atmospheric and oceanic technologyen_US
dc.rights© 2014 American Meteorological Society. This paper was published in Journal of Atmospheric and Oceanic Technology and is made available as an electronic reprint (preprint) with permission of American Meteorological Society. The paper can be found at the following official DOI: [http://dx.doi.org/10.1175/JTECH-D-13-00048.1].  One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.en_US
dc.subjectDRNTU::Engineering::Environmental engineering
dc.titleCloud classification of ground-based images using texture–structure featuresen_US
dc.typeJournal Article
dc.identifier.doihttp://dx.doi.org/10.1175/JTECH-D-13-00048.1
dc.description.versionPublished versionen_US
dc.contributor.organizationInstitute for Media Innovationen_US


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