Articles | Volume 10, issue 1
https://doi.org/10.5194/amt-10-199-2017
https://doi.org/10.5194/amt-10-199-2017
Research article
 | 
17 Jan 2017
Research article |  | 17 Jan 2017

Cloud detection in all-sky images via multi-scale neighborhood features and multiple supervised learning techniques

Hsu-Yung Cheng and Chih-Lung Lin

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Cited articles

Bay, H, Ess, A., Tuytelaars, T., and Gool, L. V.: SURF: Speeded Up Robust Features, Comput. Vis. Image Und., 110, 346–359, 2008.
Bernecker, D., Riess, C., Christlein, V., Angelopoulou, E., and Hornegger, J.: Representation learning for cloud classification, Lect. Notes Comput. Sc., 8142, 395–404, 2013.
Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J.: Classification and regression trees, Wadsworth & Brooks/Cole Advanced Books & Software, Monterey, CA, USA, 1984.
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.: All-sky Images, National Central University, https://drive.google.com/open?id=0B38yagaBviZYNmxReVBIQkVJYkk, last access: 10 January 2017.
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Short summary
A cloud detection method for all-sky images is proposed. Obtaining improved cloud detection results is helpful for cloud classification, tracking and solar irradiance prediction. The features are extracted from local image patches with different sizes to include local structure and multi-resolution information. The cloud models are learned through the training process. We have shown that taking advantages of multiple classifiers and various patch sizes is able to increase the detection accuracy.