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Sensors 2015, 15(2), 4176-4192; doi:10.3390/s150204176

Compressive SAR Imaging with Joint Sparsity and Local Similarity Exploitation

School of Electronic Engineering, Xidian University, Xi'an 710071, China
Author to whom correspondence should be addressed.
Received: 20 October 2014 / Accepted: 2 February 2015 / Published: 12 February 2015
(This article belongs to the Section Physical Sensors)
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Compressive sensing-based synthetic aperture radar (SAR) imaging has shown its superior capability in high-resolution image formation. However, most of those works focus on the scenes that can be sparsely represented in fixed spaces. When dealing with complicated scenes, these fixed spaces lack adaptivity in characterizing varied image contents. To solve this problem, a new compressive sensing-based radar imaging approach with adaptive sparse representation is proposed. Specifically, an autoregressive model is introduced to adaptively exploit the structural sparsity of an image. In addition, similarity among pixels is integrated into the autoregressive model to further promote the capability and thus an adaptive sparse representation facilitated by a weighted autoregressive model is derived. Since the weighted autoregressive model is inherently determined by the unknown image, we propose a joint optimization scheme by iterative SAR imaging and updating of the weighted autoregressive model to solve this problem. Eventually, experimental results demonstrated the validity and generality of the proposed approach. View Full-Text
Keywords: SAR; compressive sensing; adaptive sparse representation; random sensing measurements SAR; compressive sensing; adaptive sparse representation; random sensing measurements

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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MDPI and ACS Style

Shen, F.; Zhao, G.; Shi, G.; Dong, W.; Wang, C.; Niu, Y. Compressive SAR Imaging with Joint Sparsity and Local Similarity Exploitation. Sensors 2015, 15, 4176-4192.

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