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Remote Sens. 2017, 9(8), 795; doi:10.3390/rs9080795

Refocusing of Moving Targets in SAR Images via Parametric Sparse Representation

1
Institute of Information and Navigation, Air Force Engineering University, Xi’an 710077, China
2
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
3
Key Laboratory for Information Science of Electromagnetic Wave (Ministry of Education), Fudan University, Shanghai 200433, China
4
School of Electronic and Information Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
*
Author to whom correspondence should be addressed.
Academic Editors: Xiaofeng Yang, Xiaofeng Li, Ferdinando Nunziata and Alexis Mouche
Received: 12 June 2017 / Revised: 28 July 2017 / Accepted: 31 July 2017 / Published: 2 August 2017
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
View Full-Text   |   Download PDF [6767 KB, uploaded 2 August 2017]   |  

Abstract

In this paper, a parametric sparse representation (PSR) method is proposed for refocusing of moving targets in synthetic aperture radar (SAR) images. In regular SAR images, moving targets are defocused due to unknown motion parameters. Refocusing of moving targets requires accurate phase compensation of echo data. In the proposed method, the region of interest (ROI) data containing the moving targets are extracted from the complex SAR image and represented in a sparse fashion through a parametric transform, which is related to the phase compensation parameter. By updating the reflectivities of moving target scatterers and the parametric transform in an iterative fashion, the phase compensation parameter can be accurately estimated and the SAR images of moving targets can be refocused well. The proposed method directly operates on small-size defocused ROI data, which helps to reduce the computational burden and suppress the clutter. Compared to other existing ROI-based methods, the proposed method can suppress asymmetric side-lobes and improve the image quality. Both simulated data and real SAR data collected by GF-3 satellite are used to validate the effectiveness of the proposed method. View Full-Text
Keywords: moving target imaging; parametric sparse representation (PSR); region of interest (ROI); synthetic aperture radar (SAR) moving target imaging; parametric sparse representation (PSR); region of interest (ROI); synthetic aperture radar (SAR)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Chen, Y.; Li, G.; Zhang, Q.; Sun, J. Refocusing of Moving Targets in SAR Images via Parametric Sparse Representation. Remote Sens. 2017, 9, 795.

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