Simultaneous buried object detection and imaging technique utilizing fuzzy weighted background calculation and target energy moments on ground penetrating radar data

Sezgin, Mehmet

doi:10.1186/1687-6180-2011-55

Cited by 11 publications

(7 citation statements)

References 17 publications

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“…Several researchers have already put forward a number of methods to reduce background clutter and noise in GPR data sets, such as: simple mean scan subtraction (55); complex average subtraction (56); moving average background removal (57); HHkkkHKalman Filter-Based approach to Target-Background Separation (58); background removal using Singular Value Decomposition (SVD) (54), Eigenvalues (59); the likelihood ratio test (55,60); the whitening filter (61); the median filter (62); the fuzzy weighted background removal and target energy function approach (63,64); and the wavelet-based method (65,66).…”

Section: Related Literature Reviewmentioning

confidence: 99%

Nondestructive Evaluation of the Condition of Subsurface Drainage in Pavements Using Ground Penetrating Radar (GPR)

Sinfield¹,

Bai²

2017

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The authors would like to thank Dwayne Harris for his support and expert guidance during the field work required for this study as well as his constructive review of the study findings. In addition, the authors appreciated the counsel provided by the Study Advisory Committee members throughout this effort. JOINT TRANSPORTATION RESEARCH PROGRAM The Joint Transportation Research Program serves as a vehicle for INDOT collaboration with higher education institutions and industry in Indiana to facilitate innovation that results in continuous improvement in the planning, design, construction, operation, management and economic efficiency of the Indiana transportation infrastructure.

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Section: Related Literature Reviewmentioning

confidence: 99%

Nondestructive Evaluation of the Condition of Subsurface Drainage in Pavements Using Ground Penetrating Radar (GPR)

Sinfield¹,

Bai²

2017

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“…Essam A Rashed proposed in (Sezgin 2011). Very recently, alternative seismic stacking techniques (Rashed 2013) and background matrix subtraction (Rashed and Harbi 2014) were presented.…”

Section: Gpr Background Removal Using a Directional Total Variation Mmentioning

confidence: 99%

GPR background removal using a directional total variation minimisation approach

Rashed

2015

J. Geophys. Eng.

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Ground penetrating radar (GPR) is a leading geophysical subsurface imaging tool for various purposes. This efficiency, however, is compromised by the interference of different types of noise. Background noise (clutter) is one of the nagging types of noise that undermines the high-resolution imaging capabilities of GPR. This study presents the experience of applying a directional total variation minimisation (DTVM) filter to attenuate clutter in GPR data. The application of DTVM to both synthetic and field GPR data proves its great capability to attenuate clutter without affecting the features of interest of a GPR section. The results also show that the proposed DTVM method affords superior image quality than both the most commonly used and the most recently published background removal techniques.

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“…), multi‐resolution wavelet analysis (Jeng et al . ) and fuzzy weighted background calculation (Sezgin ). However, the most commonly used algorithm is background removal or average trace subtraction (Nobes ).…”

Section: Introductionmentioning

confidence: 99%

Application of alternative seismic‐stacking techniques to ringing noise removal from GPR data

Rashed

2013

Near Surface Geophysics

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Ringing is a common type of coherent noise that degrades the quality of ground‐penetrating radar data. Conventional ringing attenuation or a background removal algorithm works by generating an average ensemble that is then subtracted from all traces in a GPR section. This study presents four alternative algorithms, usually used in stacking‐seismic reflection data, for generating such an ensemble. These algorithms include median stack, diversity stack, alpha‐trimmed stack and smart stack. The traditional algorithm and the four alternative algorithms are tested using two real GPR data sets. The outcomes of the five background removal methods are compared to each other and to the original data using statistical analyses and visual inspection. The results show that all the four alternative techniques are more efficient in background removal than the conventional technique, with the smart ensemble yielding the best results followed by the alpha‐trimmed ensemble.

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Simultaneous buried object detection and imaging technique utilizing fuzzy weighted background calculation and target energy moments on ground penetrating radar data

Cited by 11 publications

References 17 publications

Nondestructive Evaluation of the Condition of Subsurface Drainage in Pavements Using Ground Penetrating Radar (GPR)

Nondestructive Evaluation of the Condition of Subsurface Drainage in Pavements Using Ground Penetrating Radar (GPR)

GPR background removal using a directional total variation minimisation approach

Application of alternative seismic‐stacking techniques to ringing noise removal from GPR data

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