X2Ka Translation Network: Mitigating Ka-Band PolSAR Data Insufficiency via Neural Style Transfer

Data insufficiency poses a significant challenge in Ka-band polarimetric synthetic aperture radar (PolSAR) applications. Traditional PolSAR simulation approaches fail to conquer this issue due to the intricate modeling and computational complexities induced by high-frequency. In this article, the authors propose to mitigate this issue through neural style transfer (NST). An X2Ka translation network is proposed to transfer X-band PolSAR images to Ka-band. Leveraging the well-verified generative network Pix2Pix, the authors adapt it to accommodate the specific discrepancies between PolSAR and optical data. Experiments are conducted on X- and Ka-bands PolSAR images acquired by an Airborne PolSAR system from the Chinese Academy of Sciences. Both qualitative and quantitative evaluation results demonstrate the effectiveness of the proposed network.