Cloud screening with combined MERIS and AATSR images
This paper presents a cloud screening algorithm based on ensemble methods that exploits the combined information from both MERIS and AATSR instruments on board ENVISAT in order to improve current cloud masking products for both sensors. The first step is to analyze the synergistic use of MERIS and AATSR images in order to extract some physically-based features increasing the separability of clouds and surface. Then, several artificial neural networks are trained using different sets of input features and different sets of training samples depending on acquisition and surface conditions. Finally, outputs of the trained neural networks are combined at the decision level to construct a more accurate and robust ensemble of classifiers. The proposed classifier is tested on more than 80 coregistered MERIS/AATSR images providing better classification accuracy than the official cloud flags and available operational cloud screening algorithms for MERIS and AATSR. Moreover, thanks to the synergy of both sensors, it correctly classifies critical cloud-screening problems such as snow and ice covers over land and sun-glint over ocean.