Generalized Zero-Shot Recognition of SAR Target via Conditional Disentangled Learning

Most of the existing methods for automatic target recognition (ATR) in synthetic aperture radar (SAR) images only focus on the seen classes in the testing stage, but ignore the possibility of the unseen classes. To address this problem, a generalized zero-shot SAR target recognition method based on conditional disentangled representation learning is proposed, which can be used for both seen and unseen classes. The recognition framework is based on the variational autoencoders (VAE) to disentangle the continuous and discrete latent variables. The unseen classes are recognized by continuous variables, while the discrete ones are used for the seen classes. In addition, the class labels of seen classes are incorporated as condition information to improve the inter-class discrimination in two disentangled variable spaces. The generalized zero-shot experiment results show that the proposed method achieves considerable recognition accuracy for both seen and unseen classes.