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A conditional random field (CRF) model for cloud detection in ground basedsky images is presented. We show that very high cloud detection accuracy can beachieved by combining a discriminative classifier and a higher order cliquepotential in a CRF framework. The image is first divided into homogeneousregions using a mean shift clustering algorithm and then a CRF model is definedover these regions. The various parameters involved are estimated usingtraining data and the inference is performed using Iterated Conditional Modes(ICM) algorithm. We demonstrate how taking spatial context into account canboost the accuracy. We present qualitative and quantitative results to provethe superior performance of this framework in comparison with other state ofthe art methods applied for cloud detection.

A Conditional Random Field Model for Context Aware Cloud Detection in Sky Images