A Novel Category Discovery Method for SAR Images Based on an Improved UNO Framework

In recent years, synthetic aperture radar automatic target recognition (SAR ATR) has been widely researched for its ability to achieve high-performance target classification through supervised training, facilitating battlefield reconnaissance, and intelligence generation. When dealing with unknown class data for ATR model training, significant time and effort are typically required for manual interpretation and labeling. However, when unknown class data shares the same domain as the known labeled data in the library, leveraging their shared deep semantic knowledge can enable automatic classification and labeling of the unknown class data. In this article, we investigate the novel category discovery (NCD) problem in SAR images, using labeled data to guide the clustering process of new class data. Specifically, we utilize the Unified Objective function training framework to address the training imbalance between labeled and unlabeled data in the NCD process, incorporating various improvements on this foundation. Through a binary segmentation-based strategy, we effectively mitigate the interference of “noise pairs” with significant semantic differences on the model's self-supervised pretraining. In addition, we introduce multicrop consistency loss and equal distance loss to impose constraints on training by leveraging intraclass and interclass relationships in the latent space, thereby obtaining representations with higher interclass separability. Our method achieves state-of-the-art clustering performance in multiple scenarios. Extensive experimental results on the MSTAR benchmark dataset demonstrate the effectiveness of the proposed methods.