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Proceedings Paper

Analysis and suppression of bias effect in sparse SAR imaging
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Paper Abstract

The analytic solution of sparse signal reconstruction algorithm based on L1 regularization is a biased estimation, which leads to the underestimation of target intensity when applied to sparse SAR imaging, resulting in the bias effect and affecting the reconstruction accuracy. In this paper, we quantitatively analyze the bias effect in SAR imaging applications, and analyse the influence of target intensity, signal-to-noise ratio, intensity ratio of adjacent targets in the observation scene on the reconstruction bias. In order to suppress the bias effect and improve the reconstruction accuracy, we adopt a class of algorithms based on nonconvex penalty, and verify the performance of these algorithms using simulations and real data.

Paper Details

Date Published: 7 October 2019
PDF: 12 pages
Proc. SPIE 11155, Image and Signal Processing for Remote Sensing XXV, 111551E (7 October 2019); doi: 10.1117/12.2532446
Show Author Affiliations
Zhongqiu Xu, Institute of Electronics (China)
Zhonghao Wei, Institute of Electronics (China)
Mingqian Liu, Institute of Electronics (China)
Bingchen Zhang, Institute of Electronics (China)
Yirong Wu, Institute of Electronics (China)

Published in SPIE Proceedings Vol. 11155:
Image and Signal Processing for Remote Sensing XXV
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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