Share Email Print
cover

Proceedings Paper

Deep learning for SAR image formation
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

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.

Paper Details

Date Published: 28 April 2017
PDF: 11 pages
Proc. SPIE 10201, Algorithms for Synthetic Aperture Radar Imagery XXIV, 1020104 (28 April 2017); doi: 10.1117/12.2267831
Show Author Affiliations
Eric Mason, Rensselaer Polytechnic Institute (United States)
Bariscan Yonel, Rensselaer Polytechnic Institute (United States)
Birsen Yazici, Rensselaer Polytechnic Institute (United States)


Published in SPIE Proceedings Vol. 10201:
Algorithms for Synthetic Aperture Radar Imagery XXIV
Edmund Zelnio; Frederick D. Garber, Editor(s)

© SPIE. Terms of Use
Back to Top
PREMIUM CONTENT
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?
close_icon_gray