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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 3, issue 3
Atmos. Meas. Tech., 3, 557–567, 2010
https://doi.org/10.5194/amt-3-557-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Meas. Tech., 3, 557–567, 2010
https://doi.org/10.5194/amt-3-557-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.

  06 May 2010

06 May 2010

Automatic cloud classification of whole sky images

A. Heinle1, A. Macke2, and A. Srivastav1 A. Heinle et al.
  • 1Excellence Cluster "The Future Ocean", Department of Computer Science, Kiel University, Kiel, Germany
  • 2Leibniz Institute of Marine Sciences at Kiel University (IFM-GEOMAR), Kiel, Germany

Abstract. The recently increasing development of whole sky imagers enables temporal and spatial high-resolution sky observations. One application already performed in most cases is the estimation of fractional sky cover. A distinction between different cloud types, however, is still in progress. Here, an automatic cloud classification algorithm is presented, based on a set of mainly statistical features describing the color as well as the texture of an image. The k-nearest-neighbour classifier is used due to its high performance in solving complex issues, simplicity of implementation and low computational complexity. Seven different sky conditions are distinguished: high thin clouds (cirrus and cirrostratus), high patched cumuliform clouds (cirrocumulus and altocumulus), stratocumulus clouds, low cumuliform clouds, thick clouds (cumulonimbus and nimbostratus), stratiform clouds and clear sky. Based on the Leave-One-Out Cross-Validation the algorithm achieves an accuracy of about 97%. In addition, a test run of random images is presented, still outperforming previous algorithms by yielding a success rate of about 75%, or up to 88% if only "serious" errors with respect to radiation impact are considered. Reasons for the decrement in accuracy are discussed, and ideas to further improve the classification results, especially in problematic cases, are investigated.

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