Unsupervised segmentation of low clouds from infrared METEOSAT images based on a contextual spatio-temporal labeling approach

The early and accurate segmentation of low clouds during the night-time is an important task for nowcasting. It requires that observations can be acquired at a sufficient time rate as provided by the geostationary METEOSAT satellite over Europe. However, the information supplied by the single infrared METEOSAT channel available by night is not sufficient to discriminate between low clouds and ground during night from a single image. To tackle this issue, the authors consider several sources of information extracted from an infrared image sequence. Indeed, they exploit both relevant local motion-based measurements, intensity images and thermal parameters estimated over blocks, along with local contextual information. A statistical contextual labeling process in two classes, involving "low clouds" and "clear sky," is performed on the warmer pixels. It is formulated within a Bayesian estimation framework associated with Markov random field (MRF) models. This comes to minimize a global energy function comprising three terms: two data-driven terms (thermal and motion-based ones) and a regularization term expressing a priori knowledge on the label field (expected spatial contextual properties). The authors propose a progressive minimization procedure of this energy function starting from initial reliably labeled pixels and involving only local computation