This paper proposes a generative model based semi-supervised learning method of remote sensing image classification

which makes use of both the labeled and unlabeled samples to handle the insufficient labeled training samples problems.We first train an original classifier by the small number of labeled samples alone.Then we re-train it by both the labeled and a large amount of unlabeled samples.This process is iterated until the likelihood function of all the samples are converged to the local maxima.Through the designed experiments of the two different mixture models

It is found that the unlabeled samples help us to get the method to enhance the classification performance to a large extent on condition

which the ratio of the unlabeled samples to the labeled ones must be appropriate.Thus

we have also compared the method by using the state-of-the-art support vector machines（SVMs） with the same labeled samples

of which results show that our method works better.