Feature extraction and classification of echo signal of ground penetrating radar

2005 ◽  
Vol 10 (6) ◽  
pp. 1009-1012 ◽  
Author(s):  
Zhou Hui-lin ◽  
Tian Mao ◽  
Chen Xiao-li
Keyword(s):  
Feature Extraction ◽  
Ground Penetrating Radar ◽  
Echo Signal ◽  
Ground Penetrating

scholarly journals Feature Extraction and Automatic Material Classification of Underground Objects from Ground Penetrating Radar Data

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Qingqing Lu ◽  
Jiexin Pu ◽  
Zhonghua Liu
Keyword(s):  
Feature Extraction ◽  
Ground Penetrating Radar ◽  
Classification Accuracy ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Noisy Environment ◽  
Feature Based ◽  
Approximation Coefficients ◽  
Ground Penetrating

Ground penetrating radar (GPR) is a powerful tool for detecting objects buried underground. However, the interpretation of the acquired signals remains a challenging task since an experienced user is required to manage the entire operation. Particularly difficult is the classification of the material type of underground objects in noisy environment. This paper proposes a new feature extraction method. First, discrete wavelet transform (DWT) transforms A-Scan data and approximation coefficients are extracted. Then, fractional Fourier transform (FRFT) is used to transform approximation coefficients into fractional domain and we extract features. The features are supplied to the support vector machine (SVM) classifiers to automatically identify underground objects material. Experiment results show that the proposed feature-based SVM system has good performances in classification accuracy compared to statistical and frequency domain feature-based SVM system in noisy environment and the classification accuracy of features proposed in this paper has little relationship with the SVM models.

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 Underground Object Classification for Urban Roads Using Instantaneous Phase Analysis of Ground-Penetrating Radar (GPR) Data

2018 ◽  
Vol 10 (9) ◽  
pp. 1417 ◽  
Author(s):  
Byeongjin Park ◽  
Jeongguk Kim ◽  
Jaesun Lee ◽  
Man-Sung Kang ◽  
Yun-Kyu An
Keyword(s):  
Phase Analysis ◽  
Ground Penetrating Radar ◽  
Data Interpretation ◽  
Buried Pipes ◽  
Instantaneous Phase ◽  
Analysis Technique ◽  
Different Types ◽  
Ground Penetrating ◽  
Rapid Scanning

Ground-penetrating radar (GPR) has been widely used to detect subsurface objects, such as hidden cavities, buried pipes, and manholes, owing to its noncontact sensing, rapid scanning, and deeply penetrating remote-sensing capabilities. Currently, GPR data interpretation depends heavily on the experience of well-trained experts because different types of underground objects often generate similar GPR reflection features. Moreover, reflection visualizations that were obtained from field GPR data for urban roads are often weak and noisy. This study proposes a novel instantaneous phase analysis technique to address these issues. The proposed technique aims to enhance the visibility of underground objects and provide objective criteria for GPR data interpretation so that the objects can be automatically classified without expert intervention. The feasibility of the proposed technique is validated both numerically and experimentally. The field test utilizes rarely available GPR data for urban roads in Seoul, South Korea and demonstrates that the technique allows for successful visualization and classification of three different types of underground objects.

Identification, investigation and classification of surface depressions and chalk dissolution features using integrated LiDAR and geophysical methods

2020 ◽  
Vol 53 (4) ◽  
pp. 620-644 ◽  
Author(s):  
Zoe Elizabeth Jeffery ◽  
Stephen Penn ◽  
David Peter Giles ◽  
Linley Hastewell
Keyword(s):  
Ground Penetrating Radar ◽  
Image Interpretation ◽  
Geophysical Methods ◽  
Integrated Approach ◽  
Study Data ◽  
Aerial Photographs ◽  
Geophysical Techniques ◽  
Karstic Features ◽  
Ground Penetrating

The chalk bedrock of the Hampshire Basin, southern England is an important aquifer and is highly susceptible to dissolution, making the development and presence of karstic features a widespread occurrence. These features are hazardous because they provide possible pathways to the underlying aquifer and therefore present potential site-specific contamination risks. There is also evidence of extensive extraction, through both mining and surface quarrying, of chalk, flint and clay over many centuries. Geophysical techniques consisting of electromagnetic (EM31) and ground-penetrating radar surveys were used to identify and characterize target features identified from desk study data. The ground-penetrating radar and EM31 interpretations allowed the classification of non-anthropogenic target features, such as diffuse buried sinkholes with disturbed and subsiding clay-rich infill and varying symmetrical and asymmetrical morphologies. We describe here the investigations of such features identified at Holme Farm, Stansted House, Hampshire. The combination of EM31 data and ground-penetrating radar profiles facilitated the identification of a palaeovalley, cavities and irregular rockhead. This investigation identified locations of aquifer contamination risk as some sinkholes have been sites for the illegal dumping of waste or the infiltration of fertilizers, leaking sewage pipes or animal waste. This potential source of contamination utilizes the sinkhole as a pathway into the highly transmissive White Chalk Subgroup of Hampshire and has caused contamination of the aquifer. We conclude that our integrated approach of geophysical techniques linked to aerial photographs and LiDAR image interpretation was highly effective in the location and characterization of dissolution structures, infilled former quarries and mining features at this site.

scholarly journals Automatic Classification of Ground-Penetrating-Radar Signals for Railway-Ballast Assessment

2011 ◽  
Vol 49 (10) ◽  
pp. 3961-3972 ◽  
Author(s):  
Wenbin Shao ◽  
Abdesselam Bouzerdoum ◽  
Son Lam Phung ◽  
Lijun Su ◽  
Buddhima Indraratna ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Automatic Classification ◽  
Railway Ballast ◽  
Radar Signals ◽  
Ground Penetrating
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