scholarly journals Ground penetrating Radar Clutter Removal via 1D Fast Sub band Decomposition

2019 ◽  
Vol 69 (1) ◽  
pp. 74-79
Author(s):  
Deniz Kumlu ◽  
Gökhan Karasakal ◽  
Nur Hüseyin Kaplan ◽  
Isin Erer
Keyword(s):  
Wavelet Transform ◽  
Ground Penetrating Radar ◽  
Decomposition Methods ◽  
Simulation Software ◽  
Target Component ◽  
Subband Decomposition ◽  
Decomposition Scheme ◽  
Multi Scale ◽  
Clutter Removal ◽  
Ground Penetrating

Target detection performance in ground-penetrating radar (GPR) deteriorates highly in the presence of clutter. Multi-scale (wavelet transform) or the recently proposed multi-scale and multi-directional decomposition based methods can efficiently remove the clutter, however they have high computational complexity. In this paper, we propose a new multi-scale method which requires only 1D fast subband decomposition of the rows of the GPR image. The resulting detail layers directly provide the clutter-free target component of the GPR image. The proposed method is compared to the state-of-art clutter removal methods both visually and quantitatively using a realistic simulated dataset which is constructed by the gprMax simulation software. The results show that the proposed 1D subband decomposition scheme approximates the classical 2D wavelet decomposition successfully and even presents a performance increase as well as a complexity decrease for fast decomposition methods based on lifting wavelet transform and a trous wavelet transform.

Clutter Removal Techniques in Ground Penetrating Radar for Landmine Detection

2019 ◽  
pp. 375-399 ◽  
Author(s):  
Deniz Kumlu ◽  
Isin Erer
Keyword(s):  
Performance Analysis ◽  
Soil Properties ◽  
Ground Penetrating Radar ◽  
Simulation Software ◽  
Landmine Detection ◽  
Simulated Dataset ◽  
Detection Scheme ◽  
Military Organizations ◽  
Clutter Removal ◽  
Ground Penetrating

Ground-penetrating radar (GPR) is a popular technique for landmine detection and widely used by military organizations for landmine clearance purposes. It is well known that GPR is greatly affected by clutter during the landmine detection process. The clutter can be reasoned by soil properties, depth of the buried landmine, different surface types, and ingredient of landmine materials. Thus, the detection of landmine becomes challenging, and clutter removal algorithm must be applied prior to any landmine detection scheme in GPR. In order to remove clutter, various algorithms are proposed, and they can be mainly separated into two groups such subspace-based methods and multiresolution-based methods. This chapter focuses on the performance analysis of these clutter removal algorithms on the simulated dataset that is created by using the gprMax simulation software where it contains four different challenging scenarios.

scholarly journals Particle Center Supported Plane for Subsurface Target Classification based on Full Polarimetric Ground Penetrating Radar

2019 ◽  
Vol 11 (4) ◽  
pp. 405
Author(s):  
Xuan Feng ◽  
Haoqiu Zhou ◽  
Cai Liu ◽  
Yan Zhang ◽  
Wenjing Liang ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Decomposition Methods ◽  
Support Vector ◽  
Classification Methods ◽  
Target Classification ◽  
Data Set ◽  
Particle Center ◽  
Ground Penetrating ◽  
New Criterion

The subsurface target classification of ground penetrating radar (GPR) is a popular topic in the field of geophysics. Among the existing classification methods, geometrical features and polarimetric attributes of targets are primarily used. As polarimetric attributes contain more information of targets, polarimetric decomposition methods, such as H-Alpha decomposition, have been developed for target classification of GPR in recent years. However, the classification template used in H-Alpha classification is preset depending on the experience of synthetic aperture radar (SAR); therefore, it may not be suitable for GPR. Moreover, many existing classification methods require excessive human operation, particularly when outliers exist in the sample (the data set containing the features of targets); therefore, they are not efficient or intelligent. We herein propose a new machine learning method based on sample centers, i.e., particle center supported plane (PCSP). The sample center is defined as the point with the smallest sum of distances from all points in the same sample, which is considered as a better representation of the sample without significant effect of the outliers. In this proposed method, particle swarm optimization (PSO) is performed to obtain the sample centers; the new criterion for subsurface target classification is achieved. We applied this algorithm to full polarimetric GPR data measured in the laboratory and outdoors. The results indicate that, comparing with support vector machine (SVM) and classical H-Alpha classification, this new method is more efficient and the accuracy is relatively high.

scholarly journals Study on Wavelet Entropy for Airport Pavement Inspection using a Multi-Static GPR System

Geophysics ◽  
2021 ◽  
pp. 1-74
Author(s):  
Lilong Zou ◽  
Kazutaka Kikuta ◽  
Amir M. Alani ◽  
Motoyuki Sato
Keyword(s):  
Wavelet Transform ◽  
Ground Penetrating Radar ◽  
Large Area ◽  
New Approach ◽  
Wavelet Entropy ◽  
High Entropy ◽  
Airport Pavement ◽  
Base Experiment ◽  
Pavement Structures ◽  
Ground Penetrating

The multi-layer nature of airport pavement structures is susceptible to the generation of voids at the bonding parts of the structure, which is also called interlayer debonding. Observations have shown that the thickness of the resulting voids is usually at the scale of millimeters, which makes it difficult to inspect. The efficient and accurate characteristics of ground penetrating radar (GPR) make it suitable for large area inspections of airport pavement. In this study, a multi-static GPR system was used to inspect the interlayer debonding of a large area of an airport pavement. A special antenna arrangement can obtain common mid-point (CMP) gathers during a common offset survey. The presence of interlayer debonding affects the phase of the reflection signals, and the phase disturbance can be quantified by wavelet transform. Therefore, an advanced approach that uses the average entropy of the wavelet transform parameters in CMP gathers to detect the interlayer debonding of airport pavement is proposed. The results demonstrate that the regions with high entropy correspond to the regions where tiny voids exist. The new approach introduced in this study was then evaluated by a field-base experiment at an airport taxiway model. The results show that the proposed approach can detect interlayer debonding of the pavement model accurately and efficiently. The on-site coring results confirm the performance of the proposed approach.

scholarly journals Integration of ground-penetrating radar, ultrasonic tests and infrared thermography for the analysis of a precious medieval rose window

2010 ◽  
Vol 24 ◽  
pp. 69-82 ◽  
Author(s):  
L. Nuzzo ◽  
A. Calia ◽  
D. Liberatore ◽  
N. Masini ◽  
E. Rizzo
Keyword(s):  
Infrared Thermography ◽  
Ground Penetrating Radar ◽  
Integrated Approach ◽  
13Th Century ◽  
Complementary Information ◽  
Multi Scale ◽  
Architectural Elements ◽  
Non Invasive ◽  
Ground Penetrating ◽  
The Rose

Abstract. The integration of high-resolution, non-invasive geophysical techniques (such as ground-penetrating radar or GPR) with emerging sensing techniques (acoustics, thermography) can complement limited destructive tests to provide a suitable methodology for a multi-scale assessment of the state of preservation, material and construction components of monuments. This paper presents the results of the application of GPR, infrared thermography (IRT) and ultrasonic tests to the 13th century rose window of Troia Cathedral (Apulia, Italy), affected by widespread decay and instability problems caused by the 1731 earthquake and reactivated by recent seismic activity. This integrated approach provided a wide amount of complementary information at different scales, ranging from the sub-centimetre size of the metallic joints between the various architectural elements, narrow fractures and thin mortar fillings, up to the sub-metre scale of the internal masonry structure of the circular ashlar curb linking the rose window to the façade, which was essential to understand the original building technique and to design an effective restoration strategy.

Qualitative research: The impact of root orientation on coarse roots detection using ground-penetrating radar (GPR)

BioResources ◽  
2020 ◽  
Vol 15 (2) ◽  
pp. 2237-2257
Author(s):  
Mingkai Wang ◽  
Jian Wen ◽  
Wenbin Li
Keyword(s):  
Ground Penetrating Radar ◽  
Simulation Experiment ◽  
Simulation Software ◽  
Dielectric Constants ◽  
Three Dimensions ◽  
Coarse Roots ◽  
Coarse Root ◽  
Ground Penetrating ◽  
The Impact ◽  
Root Orientation

The growth of coarse roots is complex, leading to intricate patterns of root systems in three dimensions. To detect and recognize coarse roots, ground-penetrating radar (GPR) was used. According to the GPR theory, a clear profile hyperbola is formed on the GPR radargrams when electromagnetic waves travel across two surfaces with different dielectric constants. First, the forward models (different root orientations) were built with simulation software (GprMax3.0) based on the finite-different time-domain method (FDTD). As the radar moved forward, the signal reflection curve was generated in different root orientations. An algorithm was proposed to obtain the coordinates of a single coarse root and analyze the influence of root direction on the hyperbola of coarse root through a symmetry curve and relative error (RE). Based on GPR datasets from the simulation experiment, the controlled experiment evaluated feasibility and effectiveness of the simulation experiment. To demonstrate the effect of the root orientation, the algorithm was applied to in situ recognition of the Summer Palace. The results showed that the localization of root orientation was relatively accurate. However, the proposed algorithm was unable to implement automatic detection, and the results still required human intervention. This research provides a solid basis for the biomass measurement, diameter estimation, and especially the three-dimensional reconstruction of ancient and famous trees.

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