Automatic cloud classification of whole sky images
Open Access
- 6 May 2010
- journal article
- Published by Copernicus GmbH in Atmospheric Chemistry and Physics
- Vol. 3 (3), 557-567
- https://doi.org/10.5194/amt-3-557-2010
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.Keywords
This publication has 19 references indexed in Scilit:
- Estimation of the total cloud cover with high temporal resolution and parametrization of short-term fluctuations of sea surface insolationMeteorologische Zeitschrift, 2008
- Feature Extraction from Whole-Sky Ground-Based Images for Cloud-Type RecognitionJournal of Atmospheric and Oceanic Technology, 2008
- Development of a sky imager for cloud cover assessmentJournal of the Optical Society of America A, 2007
- Optimization of an Instance-Based GOES Cloud Classification AlgorithmJournal of Applied Meteorology and Climatology, 2007
- Retrieving Cloud Characteristics from Ground-Based Daytime Color All-Sky ImagesJournal of Atmospheric and Oceanic Technology, 2006
- Cloud and radiance measurements with the VIS/NIR Daylight Whole Sky Imager at Lindenberg (Germany)Meteorologische Zeitschrift, 2005
- Cloud-Base-Height Estimation from Paired Ground-Based Hemispherical ObservationsJournal of Applied Meteorology and Climatology, 2005
- Multifeature texture analysis for the classification of clouds in satellite imageryIEEE Transactions on Geoscience and Remote Sensing, 2003
- Pattern recognition techniques for the identification of cloud and cloud systemsMeteorlogical Applications, 1995
- Textural Features for Image ClassificationIEEE Transactions on Systems, Man, and Cybernetics, 1973