Gauss Mixture Model Based Semi-Supervised Classification for Remote Sensing Image

Semi-Supervised Classification,which utilizes few labeled data assigned with unlabeled data to determine classification borders,has great advantages in extracting classification information from mass data.We find Gauss mixture can well fit the remote sensing image's spectral feature space,proposed a novel thought in which each class's feature space is described by one Gauss Mixture Model,and then use the thought in Semi-Supervised Classification.A large number of experiences shows that by using a small amount of label samples,the method proposed in this paper can achieve as good classification accuracy as other supervised classification methods(such as Support Vector Machine Classification,Object Oriented Classification),which need large amount of label samples,and so has a strong application value.