Unsupervised classification of VHR panchromatic images using guided Chinese restaurant franchise mixture model

Probabilistic topic models have has successfully been used to classify remote sensing images in unsupervised way. However, the relationship among pixels is ignored in these applications because of the assuption of “bag of words”. This assuption leads to “pepper and salt effect” when these models are used to classify Very High Resolution (VHR) remote sensing images. To solve this problem, a novel model name guided Chinese Restaurant Franchise is proposed by combining the traditional Chinese Restaurant Franchise and guided information which is used to describe the relationship among pixels. Gibbs sampling method is used to infer the proposed model. The efficiency of parameters of the guided information on the result is analyzed. and then the result of our model is compared with other models. The results indicate that the proposed algorithm outperforms the other comparing models in our experiment.