Improved SAR Imaging Contour Extraction Using Smooth Sparsity-Driven Regularization

Abstract

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.