SAR Image Restoration From Spectrum Aliasing by Deep Learning

Abstract

The diversity and the space-variance of the spectrum-aliasing effects in SAR bring challenges to the available model-driven restoration methods. In this paper, a hybrid data-driven and model-driven deep learning scheme is innovatively proposed to deal with this problem. In this scheme, the SAR image restoration is realize via a U-shaped deep neural network (DNN). Meanwhile, the model of the SAR echo data is used to generate the training data to learn the weighting parameters of the network. The DNN in this method is designed to suit the SAR spectrum-aliasing application. Due to the general SAR echo mathematical model is employed for training data construction, the spectrum aliasing features in different SAR systems can be accommodated and learned. Hence, the proposed method can work well for various SAR configurations. Simulation results using the real Radarsat-1 data as well as the synthetic data prove the effectiveness as well as the robustness of the proposed method.

Keywords

• Synthetic aperture radar (SAR)
• spectrum aliasing
• restoration
• deep learning
• U-net