In this paper we present a cloud detection algorithm exploiting both the spatial and the temporal correlation of cloudy images. A region matching technique for cloud motion estimation is embodied into a MAP-MRF framework through a penalty term. We test our proposal both on simulated data and on real images acquired by MSG satellite sensors (SEVIRI) in the VIS 0.8 band. Comparisons with classical MRF based algorithms show our approach to achieve better results in terms of misclassification probability and, in particular, to be very effective in detecting cloud edges.
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