Statistical cloud detection from SEVIRI multispectral images

Cloud detection from geostationary satellite multispectral images through statistical methodologies is investigated. Discriminant analysis methods are considered to this purpose, endowed with a nonparametric density estimation and a linear transform into principal and independent components. The whole methodology is applied to the MSG-SEVIRI sensor through a set of test images covering the central and southern part of Europe. "Truth" data for the learning phase of discriminant analysis are taken from the cloud mask product MOD35 in correspondence of passages of MODIS close to the SEVIRI images. Performance of the discriminant analysis methods is estimated over sea/land, daytime/nighttime both when training and test datasets coincide and when they are different. Discriminant analysis shows very good performance in detecting clouds, especially over land. PCA and ICA are effective in improving detection.