一种信号外推和层级学习SAR超分辨率方法OA

The quality of SAR imaging was important for the downstream tasks.The classical methods were relied on the parameterized models to fit the measured data,and hence suffered from mismatch.The learning-driven methods ignored the coherent imaging mechanism and phase information.To solve these problems,a new complex-valued SAR image super-resolution method via signal extrapolation and hierarchical learning was proposed in this paper.It was composed of three phases,signal extrapolation,dual-mode cross learning,and hierarchical fusion.The spatial alignment of amplitude and phase was first achieved by the imaging operation on the zero-padded frequencies.Then,a cross learning of convolution and Transformer was employed to capture the high-level semantic information.Finally,the high-resolution SAR image was formed by feature refinement.On this basis,an evaluation system composed of the vision metrics,the imaging metrics,and the phase congruency were presented.Extensive rounds of experiments demonstrated that the proposed method improved the peak signal-to-noise ratio(PSNR)by 6.27 dB,the peak sidelobe ratio(PSLR)by 5.85 dB.