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http://dx.doi.org/10.5515/KJKIEES.2012.23.11.1307

Eigenimage-Based Signal Processing for Subsurface Inhomogeneous Clutter Reduction in Ground-Penetrating Radar Images  

Hyun, Seung-Yeup (Dept. of Telecommunications Engineering, Jeju National University)
Kim, Se-Yun (Imaging Media Research Center, Korea Institute of Science and Technology)
Publication Information
The Journal of Korean Institute of Electromagnetic Engineering and Science / v.23, no.11, 2012 , pp. 1307-1314 More about this Journal
Abstract
To reduce the effects of clutters with subsurface inhomogenities in ground-penetrating radar(GPR) images, an eigenimage based signal-processing technique is presented. If the conventional eigenimage filtering technique is applied to B-scan images of a GPR survey, relatively homogeneous clutters such as antenna ringing, direct coupling between transmitting and receiving antennas, and soil-surface reflection, can be removed sufficiently. However, since random clutters of subsurface inhomogenities still remain in the images, target signals are distorted and obscured by the clutters. According to a comparison of the eigenimage filtering results, there is different coherency between subsurface clutters and target signals. To reinforce the pixels with high coherency and reduce the pixels with low coherency, the pixel-by-pixel geometric-mean process after the eigenimage filtering is proposed here. For the validity of the proposed approach, GPR survey for detection of a metal target in a randomly inhomogeneous soil is numerically simulated by using a random media generation technique and the finite-difference time-domain(FDTD) method. And the proposed signal processing is applied to the B-scan data of the GPR survey. We show that the proposed approach provides sufficient enhancement of target signals as well as remarkable reduction of subsurface inhomogeneous clutters in comparison with the conventional eigenimage filtering.
Keywords
Ground-Penetrating Radar; Subsurface Inhomogenity; Clutter Reduction; Eigenimage Filtering;
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