Few-shot unsupervised domain adaptation for SAR target recognition based on input alignment and class confusion loss

With the development of simulation technologies and deep learning, utilizing simulated images to train Synthetic Aperture Radar (SAR) target recognition model has become a research hotspot. However, SAR target recognition is hindered by the domain gap between simulated and real images, particularly under few-shot settings which mean only a limited number of unlabeled target domain samples are available for training. Regarding this issue, this paper proposed a new few-shot Unsupervised Domain Adaptation (UDA) method based on input alignment and class confusion loss (named IACC). Our method consists of two modules. The input alignment module (IAM) utilizes a special image-to-image translation model which can realize realistic image translation in few-shot settings, while the Class Confusion Module (CCM) employs a novel loss function to align the feature space and enhance classifier performance. Experimental results on the SAMPLE dataset demonstrate the superior performance of our method in both UDA and few-shot UDA scenarios compared with other existing methods. IACC offers a promising solution for SAR target recognition in challenging domain adaptation scenarios with limited data.