Articles | Volume 8, issue 3
Atmos. Meas. Tech., 8, 1173–1182, 2015
Atmos. Meas. Tech., 8, 1173–1182, 2015

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. Cheng and C.-C. Yu

Related authors

Cloud detection in all-sky images via multi-scale neighborhood features and multiple supervised learning techniques
Hsu-Yung Cheng and Chih-Lung Lin
Atmos. Meas. Tech., 10, 199–208,,, 2017
Short summary

Related subject area

Subject: Clouds | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
Clouds over Hyytiälä, Finland: an algorithm to classify clouds based on solar radiation and cloud base height measurements
Ilona Ylivinkka, Santeri Kaupinmäki, Meri Virman, Maija Peltola, Ditte Taipale, Tuukka Petäjä, Veli-Matti Kerminen, Markku Kulmala, and Ekaterina Ezhova
Atmos. Meas. Tech., 13, 5595–5619,,, 2020
Short summary
A convolutional neural network for classifying cloud particles recorded by imaging probes
Georgios Touloupas, Annika Lauber, Jan Henneberger, Alexander Beck, and Aurélien Lucchi
Atmos. Meas. Tech., 13, 2219–2239,,, 2020
Short summary
Spatiotemporal variability of solar radiation introduced by clouds over Arctic sea ice
Carola Barrientos Velasco, Hartwig Deneke, Hannes Griesche, Patric Seifert, Ronny Engelmann, and Andreas Macke
Atmos. Meas. Tech., 13, 1757–1775,,, 2020
Short summary
Analysis algorithm for sky type and ice halo recognition in all-sky images
Sylke Boyd, Stephen Sorenson, Shelby Richard, Michelle King, and Morton Greenslit
Atmos. Meas. Tech., 12, 4241–4259,,, 2019
Short summary
Study of the diffraction pattern of cloud particles and the respective responses of optical array probes
Thibault Vaillant de Guélis, Alfons Schwarzenböck, Valery Shcherbakov, Christophe Gourbeyre, Bastien Laurent, Régis Dupuy, Pierre Coutris, and Christophe Duroure
Atmos. Meas. Tech., 12, 2513–2529,,, 2019

Cited articles

Bensmail, H. and Celeux, G.: Regularized Gaussian discriminant analysis through eigenvalue decomposition, J. Am. Stat. Assoc., 91, 1743–1748, 1996.
Cadima, J. and Jolliffe, I.: On relationships between uncentered and column-centered principal component analysis, Pak. J. Statist., 25, 473–503, 2009.
Calbo, J. and Sabburg, J.: Feature extraction from whole-sky ground-based images for cloud-type recognition, J. Atmos. Ocean. Tech., 25, 3–14, 2008.
Cheng, H. Y., Yu, C. C., Tseng, C. C., Fan, K. C., Hwang, J. N., and Jeng, B. S.: Environment classification and hierarchical lane detection for structured and unstructured roads, IET Computer Vision, 4, 37–49, 2010.
Cristianini, N. and Shawe-Taylor, J.: An introduction to support vector machines and other kernel-based learning methods, Cambridge University Press, Cambridge, England, 2000.
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.