SAR Image Autofocusing Based on Res-Unet

Wen Tang, Jiang Qian, Lu Wang, Yong Wang

Abstract: Airborne synthetic aperture radar images are easily smeared by the phase error due to the unsteady platform movement. Autofocusing by traditional methods is unsatisfied in critical condition of homogenous targets with large degree of defocusing. This paper proposes a one-step end-to-end autofocus method base on Unet with residual blocks (Res-Unet). We use smeared SAR image of a certain area of one scene for model training and test the trained network to autofocus the images of the remaining areas. Numerical experiments are conducted on real airborne SAR data and the results show that the method can achieve well-focused images for target scene with a large degree of defocusing. Comparison results also demonstrate that the proposed improved U-net structure with residual blocks far outperforms the conventional U-net in the task of SAR image autofocusing.

External IDs:dblp:conf/igarss/TangQWW22a