Transfer Adaptation Learning for Target Recognition in SAR Images: A Survey

Synthetic aperture radar (SAR) target recognition is a fundamental task in SAR image interpretation, which has made tremendous progress with the advancement of artificial intelligence technology. However, SAR imaging is sensitive to the operating conditions of platforms, resulting in large distribution discrepancy for the data collected on different platforms. Moreover, SAR target images are difficult to annotate due to the blurry textures, resulting in insufficient labeled data to train a model. Therefore, subject to the data distribution discrepancy and insufficient labeled data, SAR target recognition becomes a highly challenging task. Transfer adaptive learning (TAL) is a learning paradigm aimed at completing target tasks by transferring knowledge from relevant source domains, which is a promising technique for solving the aforementioned problems in SAR target recognition. However, there is currently no comprehensive survey about the application of transfer adaptation learning in SAR target recognition. To this end, we comprehensively summarized the development of transfer adaptive learning in SAR target recognition, and provided systematic guidance for future research. In this article, we first summarized the electromagnetic features and visual features of SAR images used for target recognition, which can be potentially used for knowledge transfer. Then, we systematically reviewed the related literature according to the homogeneity of the transfer domains, the modality of the data in the source domain, and the category of the TAL methods. The available datasets that can be used to validate the TAL methods for SAR target recognition were also summarized for the researcher's convenience. We also conducted comparative experiments on these data to demonstrate the performance of TAL methods. Finally, we analyzed the main challenges of the current methods and pointed out several directions worth studying in the future.