Generative model based semi-supervised learning method of remote sensing image classification
生成模型学习的遥感影像半监督分类

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.