Domain Adaptive Few-Shot Learning for ISAR Aircraft Recognition with Transferred Attention and Weighting Importance

Abstract

With the enhancement of air-based and space-based perception capabilities, space-aeronautics incorporation and integration is growing in importance. Full domain awareness is crucial for integrated perception systems, in which domain adaptation is one of the key problems in improving the performance of cross-domain perception. Deep learning is currently an advanced technique for complex inverse synthetic aperture radar (ISAR) object recognition. However, the training procedure needs many annotated samples, which is insufficient for certain targets, such as aircraft. Few-shot learning provides a new approach to solving the above problem by transferring useful knowledge from other domains, such as optical images from satellites. Nevertheless, it fails to fully consider the domain shift between the source and target domains, generally neglecting the transferability of training samples in the learning process. Consequently, it produces suboptimal recognition accuracy. To address the composite problems mentioned above, we propose a domain adaptive few-shot learning method from satellites to an ISAR called S2I-DAFSL for aircraft recognition tasks. Furthermore, unlike conventional domain adaptation methods that directly align the distributions, the attention transferred importance-weighting network (ATIN) is proposed to improve the transferability in the domain adaptation procedure. Compared with state-of-the-art methods, it shows that the proposed method achieves better performance, increasing the accuracy and effectiveness of classification, which is more suitable for cross-domain few-shot ISAR aircraft recognition tasks.