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Detection of Low Clouds in Meteosat Images Based on a Contextual Spatio-temporal Labeling Approach

This paper presents an original method to detect low clouds at night from METEOSAT IR images. We exploit relevant motion-based measurements as well as information on the temperature and the thermal structure of the elements constituting the scene. A classii-cation in three classes (low clouds, clear sky and other clouds) is embedded in a Bayesian estimation framework associated with Markov Random Field (MRF) models. An unsupervised learning scheme of thermal parameters associated to each class has been developed. We also propose a spatially-progressive minimization procedure of the energy function starting from reliably labelled pixels and based on the deterministic relaxation algorithm ICM. Contextual information on the label eld is taken into account. Experimental results are reported and compared with ground truth. They demonstrate the eeciency of the proposed approach.