A Deep Unsupervised Learning Method for SAR Object Classification

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SAR automatic object classification plays an important role in many SAR-based applications. Currently, the research about SAR automatic object classification focuses on supervised learning, which needs a large amount of annotated data for training the network. We find that clustering can achieve object classification based on unsupervised learning. Considering time consumption and expensive cost of manual labeling, unsupervised learning method has a prominent advantage since it does not need manually annotated data. In this paper, we introduce an unsupervised learning method to achieve SAR object classification with no labeled data. The introduced method models object clustering as a recurrent process, in which data samples are gradually clustered together according to their similarity, and feature representation of them is obtained simultaneously. The experiments on MSTAR dataset and quantitative analysis demonstrate the effectiveness of the introduced method.