Based on a CCD camera, we have developed an in-house sky imager system for the purpose of cloud cover estimation and characterization. The system captures a multispectral image every 5min5min, and the analysis is done with a method based on an optimized neural network classification procedure and a genetic algorithm.
The method discriminates between clear sky and two cloud classes: opaque and thin clouds. It also divides the image into sectors and finds the percentage of clouds in those different regions. We have validated the classification algorithm on two levels: image level, using the cloud observations included in the METAR register performed at the closest meteorological station, and pixel level, determining whether the final classification is correct.
© 2008 Optical Society of America
Cazorla, A., Olmo, F.J. and Alados-Arboledas, L., 2008. Development of a sky imager for cloud cover assessment. JOSA A, 25(1), pp.29-39.
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