Comparison of two different techniques to determine the cloud cover from all-sky imagery

All sky-cameras are devices with a very high potential in order to study atmospheric phenomena and were originally designed to obtain the cloud cover. However, methods based in different approaches produce significant differences in the results. State-of-art methods usually offer better performance, thanks to computer vision and machine learning (ML) techniques, than traditional algorithms based on channel ratios using both fixed and adaptive thresholds to classify the pixels of one image as cloud or cloud free. We have developed a cloud cover adaptive threshold algorithm base on Probability Density Function (PDF) of the Blue to Red Ratio (BRR), standing out in: simplicity; ease of implementation; compatibility with any sky-camera in terms of technical requirement and type of image acquisition. The goal of this study is to compare our algorithm with a most fashionable method based on Machine learning, discovering the pros and cons of each one and weather ultimately less can be more. The comparison has been done using a set of 1-year HDR imagery database, representing a wider range of atmospheric scenarios such as clear sky, cloudy, partly cloudy and different types of aerosol conditions and clouds such as cirrus, cumulus, stratum and nimbus. To stablish a quantitative comparison of both methods, a limited set of images has been chosen. The PDF method show better agreement than our ML implementation, with a better performance for all weather conditions, in comparison against our cloud cover database.