Articles | Volume 9, issue 2
Atmos. Meas. Tech., 9, 753–764, 2016
Atmos. Meas. Tech., 9, 753–764, 2016

Research article 01 Mar 2016

Research article | 01 Mar 2016

From pixels to patches: a cloud classification method based on a bag of micro-structures

Qingyong Li et al.

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Subject: Clouds | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
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Cited articles

Ameur, Z., Ameur, S., Adane, A., Sauvageot, H., and Bara, K.: Cloud classification using the textural features of Meteosat images, Int. J. Remote Sens., 25, 4491–4503, 2004.
Baeza-Yates,R. and Ribeiro-Neto, B.: Modern Information Retrieval, ACM Press, Addison Wesley, USA, 82 pp., 1999.
Calbo, J. and Sabburg, J.: Feature extraction from whole-sky ground-based images for cloud-type recognition, J. Atmos. Ocean. Techn., 25, 3–14, 2008.
Cheng, H.-Y. and Yu, C.-C.: Block-based cloud classification with statistical features and distribution of local texture features, Atmos. Meas. Tech., 8, 1173–1182,, 2015.
Han, J., Kamber, M., and Pei, J.: Data Mining: Concepts and Techniques, Morgan Kaufmann, San Francisco, CA, USA, 401 pp., 2006.
Short summary
This paper proposes a new cloud classification method, named bag of micro-structures (BoMS), for whole-sky imagers. BoMS treats an all-sky image as a collection of micro-structures mapped from image patches, rather than a collection of pixels. BoMS identifies five different sky conditions: cirriform, cumuliform, stratiform, clear sky, and mixed cloudiness (often appearing in all-sky images but seldom addressed in the literature). The performance of BoMS overperforms those of traditional methods.