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An algorithm of foggy image classification is presented in this paper. First, the RGB images are converted to HSI images and next we analysis the distribution of the histograms of H, S, I plane separately, from which we extract the variance of each plane under different foggy conditions as the HSI model features. Second, the dichromatic atmospheric scattering model is introduced and based on this model we develop an algorithm for computing the angular deviation of different foggy images compared to clear day image as another feature. Finally, we use this feature set to train a multi-class SVM classifier to classify four different levels of foggy images. Experiment results show that the algorithm is more than 90% accurate.

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