Tomographic SAR imaging via generative adversarial neural network with cascaded U-Net architecture

Abstract

Tomographic synthetic aperture radar is an advanced multi-channel interferometric technique for retrieving 3-D spatial information. It can be regarded as an inherently sparse reconstruction problem and can be solved using compressive sensing algorithms. However, the performances are limited by the number of acquisitions and suffer from computational burdens in practice. This paper proposes a novel method based on deep learning, which is carried out and optimized in an end-to-end manner by the generative adversarial neural networks. The proposed method applies the cascaded U-Net architectures to achieve the reconstruction of full-channel synthetic aperture radar images and the refinement of obtained tomographic results, respectively. The proposed network is trained using simulated data and validate the technique on simulated and real data. The tests show promising results with the limited number of acquisitions while reducing the computation time.