Super-Resolution Imaging Method for Synthetic Aperture Interferometric Radiometer Based on Spectral Extrapolation

Abstract

The Synthetic Aperture Interferometric Radiometer (SAIR) can realize high-resolution real-time imaging observation by using aperture synthesis technology, which has strong application advantages in the field of earth remote sensing and radio astronomy. However, due to the limitation of engineering technology, the aperture of the SAIR is still limited, which limits the further improvement of SAIR’s spatial resolution. Therefore, this letter proposes a novel super-resolution imaging method based on spectral extrapolation network (SR-SEN), which can further improve the SAIR’s imaging resolution without increasing the system hardware scale. In the SR-SEN method, the spectral extrapolation subnet is used to deduce the high-frequency spectral components from the low-frequency visibility function measured by the SAIR system, and the iterative reconstruction subnet is constructed to realize the super-resolution imaging inversion of the target scene. The simulation results show that the proposed SR-SEN method can realize accurate spectral extrapolation, improve the imaging resolution of SAIR, and finally realize high-quality imaging inversion.