The recent success of deep learning has lead to growing interest in applying these methods to signal processing problems. This paper explores the applications of deep learning to synthetic aperture radar (SAR) image formation. We review deep learning from a perspective relevant to SAR image formation. Our objective is to address SAR image formation in the presence of uncertainties in the SAR forward model. We present a recurrent auto-encoder network architecture based on the iterative shrinkage thresholding algorithm (ISTA) that incorporates SAR modeling. We then present an off-line training method using stochastic gradient descent and discuss the challenges and key steps of learning. Lastly, we show experimentally that our method can be used to form focused images in the presence of phase uncertainties. We demonstrate that the resulting algorithm has faster convergence and decreased reconstruction error than that of ISTA.
Eric Mason, Bariscan Yonel, and Birsen Yazici, "Deep learning for SAR image formation," Proc. SPIE 10201, Algorithms for Synthetic Aperture Radar Imagery XXIV, 1020104 (Presented at SPIE Defense + Security: April 13, 2017; Published: 28 April 2017); https://doi.org/10.1117/12.2267831.
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