Enhancing Few-Shot SAR Ship Recognition: Pseudospectrum Information Generation and Fusion

The limited number of samples in SAR ship datasets hampers the advancement of target recognition performance using deep learning. Given the complex-valued nature of SAR data, incorporating spectrum information is beneficial for few-shot target recognition methods. However, the SAR ship domain faces two significant issues: a scarcity of datasets that include spectrum information and a lack of target recognition networks specifically designed to leverage this spectrum information. In order to solve the above problems, first, a SpecGenGANwith generating pseudospectrum information is proposed to solve the problem of missing spectrum information. Second, a SpecAmpFusionNet (SAFN) is designed to fully exploit the deep features of spectrum and amplitude information. Finally, a few-shot target recognition method based on pseudo-spectrum information generation and fusion network is presented, allowing flexibility and integration with various popular recognition methods. Experimental results demonstrate that under 3way-10shots and 5way-10shots conditions, our method improves average accuracies by 12.04% and 10.83%, respectively, compared to methods using only amplitude information, validating the effectiveness of our approach in enhancing few-shot SAR ship recognition.