Cloud detection and classification with the use of whole-sky ground-based images

Abstract

A simple whole sky imaging system, based on a commercial digital camera with a fish-eye lens and a hemispheric dome, is used for the automatic estimation of total cloud coverage and classification. For the first time, a multi color criterion is applied on sky images, in order to improve the accuracy in detection of broken and overcast clouds under large solar zenith angles. The performance of the cloud detection algorithm is successfully compared with ground based weather observations. A simple method is presented for the detection of raindrops standing on the perimeter of hemispheric dome. Based on previous works on cloud classification, an improved k-Nearest-Neighbor algorithm is presented, based not only on statistical color and textural features, but taking also into account the solar zenith angle, the cloud coverage, the visible fraction of solar disk and the existence of raindrops in sky images. The successful detection percentage of the classifier ranges between 78 and 95% for seven cloud types.