Semi-Supervised Classification based on Gaussian Mixture Model for remote imagery

Semi-Supervised Classification (SSC), which makes use of both labeled and unlabeled data to determine classification borders in feature space, has great advantages in extracting classification information from mass data. In this paper, a novel SSC method based on Gaussian Mixture Model (GMM) is proposed, in which each class's feature space is described by one GMM. Experiments show the proposed method can achieve high classification accuracy with small amount of labeled data. However, for the same accuracy, supervised classification methods such as Support Vector Machine, Object Oriented Classification, etc. should be provided with much more labeled data.