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| Titel |
Automatic cloud classification of whole sky images |
| VerfasserIn |
A. Heinle, A. Macke, A. Srivastav |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1867-1381
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Measurement Techniques ; 3, no. 3 ; Nr. 3, no. 3 (2010-05-06), S.557-567 |
| Datensatznummer |
250001105
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| Publikation (Nr.) |
 copernicus.org/amt-3-557-2010.pdf |
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| Zusammenfassung |
| 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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