A Semisupervised Infinite Latent Dirichlet Allocation Model for Target Discrimination in SAR Images With Complex Scenes

Synthetic aperture radar (SAR) target discrimination is usually performed in a supervised manner. However, supervised methods may suffer from lack of labeled training chips, whose acquirement is costly, time-consuming, and sometimes impossible. Moreover, traditional discrimination features only provide rough and partial description about chip and perform badly in SAR images with complex scenes. In order to solve these problems, we propose a novel semisupervised target discrimination method for SAR image by combining the feature learning with classifier learning into a uniform Bayesian framework based on a modified latent Dirichlet allocation (LDA) model, semisupervised infinite latent Dirichlet allocation (SSILDA). In our method, the semisupervised idea is used to deal with the difficulty of obtaining lots of labeled chips. A new variable is introduced into the LDA model for semisupervised learning, making it possible to obtain the semantic information of chips, while implementing the target discrimination in a semantic level. Moreover, the Dirichlet process (DP) is introduced into the LDA model to automatically determine the number of topics, and the parameters are inferred via Gibbs sampling. We have analyzed the performance of the proposed method comprehensively and specifically by using some measured data and carried out comparisons with the existing methods. The results validate the effectiveness of the proposed method for SAR target discrimination.