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SAR automatic target recognition (SAR ATR) has been widely studied in recent years. Most ATR models are designed based on the traditional closed-set assumption. This type of ATR model can only identify target categories existing in the training set, and it will result in missed detection or misclassification of unseen target categories encountered in battlefield reconnaissance, posing a potential threat. Therefore, it is of great significance to design a model that can simultaneously achieve known class classification and unknown class judgment. In addition, researchers usually use the obtained unknown class data for model re-learning to enable it to recognize new categories. However, before this process, it is necessary to manually interpret and annotate the obtained unknown class data, which undoubtedly requires a large time cost and is difficult to meet the timeliness requirements. To solve these problems, we propose a framework that integrates the open-set recognition module and the novel class discovery module. By introducing the K-contrast loss, the open-set recognition module can accurately distinguish unknown class data, classify known class data, and then transfer the known class knowledge through deep clustering for clustering annotation of unknown class data. Extensive experimental results on the MSTAR benchmark dataset demonstrate the effectiveness of the proposed methods.

Open Set Recognition and Category Discovery Framework for SAR Target Classification Based on K-contrast Loss and Deep Clustering