Decorrelation-stretch based cloud detection for total sky images

Abstract

Cloud detection plays an important role in total-sky images based solar forecasting and has received more attention in recent years. Accurate cloud detection for complicated total-sky images is especially changeling due to the low contrast and vague boundaries between cloud and sky regions. Unlike the existing cloud detection method without any preprocessing, one novel decorrelation-stretch (DS) based method is proposed in this work, where the total-sky images are preprocessed using the DS algorithm firstly. With this enhancement, color feature disparity of cloud and sky can be intensified notably, and then a more accurate threshold can be obtained by applying the Minimum Cross Entropy (MCE) to the preprocessed image. Experimental results demonstrated the proposed scheme achieves better performance than the existing cloud detection methods on total-sky images, especially for images with low contrast or vague boundaries between cloud and sky regions.