电子学报2026，Vol.54Issue(2)：601-610,10.DOI:10.12263/DZXB.20250767
一种信号外推和层级学习SAR超分辨率方法
A Novel SAR Super-Resolution Method via Signal Extrapolation and Hierarchical Learning
摘要
Abstract
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.关键词
SAR超分辨率/信号外推/多尺度对齐/层级学习Key words
SAR super-resolution/signal extrapolation/multi-scale alignment/hierarchical learning分类
信息技术与安全科学引用本文复制引用
王焱,王潇,高宇洋,董刚刚..一种信号外推和层级学习SAR超分辨率方法[J].电子学报,2026,54(2):601-610,10.基金项目
国家自然科学基金(No.61971324,No.62571400) National Natural Science Foundation of China(No.61971324,No.62571400) （No.61971324,No.62571400）
