Abstract
It is necessary to detect the target reflections in ground penetrating radar (GPR) images, so that surface metal targets can be identified successfully. In order to accurately locate buried metal objects, a novel method called the Multiresolution Monogenic Signal Analysis (MMSA) system is applied in ground penetrating radar (GPR) images. This process includes four steps. First the image is decomposed by the MMSA to extract the amplitude component of the B-scan image. The amplitude component enhances the target reflection and suppresses the direct wave and reflective wave to a large extent. Then we use the region of interest extraction method to locate the genuine target reflections from spurious reflections by calculating the normalized variance of the amplitude component. To find the apexes of the targets, a Hough transform is used in the restricted area. Finally, we estimate the horizontal and vertical position of the target. In terms of buried object detection, the proposed system exhibits promising performance, as shown in the experimental results.
Highlights
As a geophysical method, ground penetrating radar (GPR) is used in the detection of the targets in near the ground surface areas for locating underground objects such as land mines and pipes
The extraction result of the first amplitude componen3t03o4f5the Multiresolution Monogenic Signal Analysis (MMSA) step is shown in Figure 6c, where the reflection signals and the noise can be suppressed and the target reflection can be enhanced after the extraction of the amplitude component
The method combines the characteristics of wavelet and energy extraction
Summary
Ground penetrating radar (GPR) is used in the detection of the targets in near the ground surface areas for locating underground objects such as land mines and pipes. Delbo et al [1] propose a procedure based on wavelets to reduce noise and a fuzzy cluster approach to detect objects. The wavelet-based amplitude components suppress noise and enhance targets in GPR images. Dong et al [7] propose synthetic aperture radar (SAR) target classification method based on Riemannian geometry. This paper aims to present a novel method for solving the object detection issues.
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