Enhancing Few-Shot SAR Ship Recognition: Pseudospectrum Information Generation and Fusion - Gui Gao; WenXi Liu; Xi Zhang (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing)
The limited number of samples in synthetic aperture radar (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 is designed to fully exploit the deep features of spectrum and amplitude information. Finally, a few-shot target recognition method based on pseudospectrum 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.