SAR Imaging Based on Deep Unfolded Network With Approximated Observation
Le Kang, Tianchi Sun, Ying Luo, Jiacheng Ni, Qun Zhang
Abstract
Compressed sensing (CS) based synthetic aperture radar (SAR) imaging methods are showing superior potential in imaging performance over classical matched filtering based methods. However, the CS-based methods require much more computational cost to solve the iterative optimization composed of large-scale matrix operators. To hold the improvement of imaging performance and reduce the computational cost, in this paper, we propose a novel SAR imaging method by Deep Unfolded Network (DUN) of Iterative Shrinkage Threshold Algorithm (ISTA) with the approximated observation of Range-Doppler Algorithm (RDA) operator. The proposed method takes the radar echoes as the input to learn the imaging procedure. Firstly, the approximated observation is utilized in SAR imaging model to reduce the size of the DUN. Moreover, we use ISTA as an example to introduce how to establish DUN with approximated observation, in which the detailed structure to handle the complex-valued radar echoes is also designed. Finally, the auto-encoder is utilized to calculate the difference of the echoes rather than the imaging results so that we can train the proposed network by unsupervised learning. The experiments of both point targets, surface targets, and real scenes show that the proposed imaging method is superior in terms of imaging performance and computing efficiency.