A SAR Open-Set Recognition Method Aided by Hierarchically Reconstructive Latent Representation Learning

Automatic target recognition (ATR) based on synthetic aperture radar (SAR) images has already obtained remarkable achievements on closed-set task. However, the recognition in a real-world scenario should not only identify the known classes but also appropriately deal with the unknown ones. To this end, we propose a SAR open-set recognition method aided by hierarchically reconstructive latent representation learning. First, a unsupervised representation learning via hierarchically-fused reconstruction network (HFRNet) is proposed to complement the lost information in supervised representation and obtain a preliminary closed-set result. Then, we adopt Openmax to correct closed-set recognition scores and give the probability of being the unknown ones, realizing the effective open-set recognition on SAR images. Finally, experimental results based on the measured dataset have shown the superior performance of the proposed method.