Improved SAR Imaging Contour Extraction Using Smooth Sparsity-Driven Regularization

Millimeter-wave imaging systems have been successfully used to detect security threats in airport checkpoints. Extracting the exact contour of the object under test from the synthetic aperture radar (SAR) image is important in order to enhance the probability of threat detection of the imaging syste...

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Bibliographic Details
Published inIEEE antennas and wireless propagation letters Vol. 15; pp. 266 - 269
Main Authors Ghazi, Galia, Rappaport, Carey M., Martinez-Lorenzo, Jose A.
Format Journal Article
LanguageEnglish
Published New York IEEE 2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Deconvolution
Image reconstruction
Imaging
inverse problems
Radar imaging
regularization
Regularization methods
Security
Synthetic aperture radar
Torso
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Summary:Millimeter-wave imaging systems have been successfully used to detect security threats in airport checkpoints. Extracting the exact contour of the object under test from the synthetic aperture radar (SAR) image is important in order to enhance the probability of threat detection of the imaging system. Unfortunately, extracting accurate contours from the SAR image is a challenging task. The latter drawback is due to blurring effect introduced by the point spread function (PSF) of the system in the SAR image. In this letter, a regularization method that promotes smooth, sparsity-driven solutions of the imaging equation is used to improve the contour extraction of the object under test. Preliminary results show that the extracted contour of the proposed approach has a root mean square (RMS) error that is 28%-35% smaller than that of the traditional, nonregularized approach.
ISSN:1536-1225
1548-5757
DOI:10.1109/LAWP.2015.2440358