Classification of buried objects based on ground-penetrating radar measurements and multiple test procedures

1999 ◽  
Author(s):  
Hakan O. Brunzell
Keyword(s):  
Ground Penetrating Radar ◽  
Test Procedures ◽  
Buried Objects ◽  
Multiple Test ◽  
Radar Measurements ◽  
Ground Penetrating

Automatic Detection and Classification of Buried Objects Using Ground-Penetrating Radar for Counter-Improvised Explosive Devices

Radio Science ◽  
2018 ◽  
Vol 53 (2) ◽  
pp. 210-227 ◽  
Author(s):  
Nattawat Chantasen ◽  
Akkarat Boonpoonga ◽  
Santana Burintramart ◽  
Krit Athikulwongse ◽  
Prayoot Akkaraekthalin
Keyword(s):  
Ground Penetrating Radar ◽  
Automatic Detection ◽  
Improvised Explosive Devices ◽  
Buried Objects ◽  
Ground Penetrating ◽  
Improvised Explosive ◽  
Explosive Devices

Laboratory test of snow wetness influence on electrical conductivity measured with ground penetrating radar

2009 ◽  
Vol 40 (1) ◽  
pp. 33-44 ◽  
Author(s):  
Nils Granlund ◽  
Angela Lundberg ◽  
James Feiccabrino ◽  
David Gustafsson
Keyword(s):  
Electrical Conductivity ◽  
Electrical Properties ◽  
Laboratory Test ◽  
Liquid Water ◽  
Ground Penetrating Radar ◽  
Wave Attenuation ◽  
Radar Measurements ◽  
Ground Penetrating ◽  
Radar Wave ◽  
The Relationship

Ground penetrating radar operated from helicopters or snowmobiles is used to determine snow water equivalent (SWE) for annual snowpacks from radar wave two-way travel time. However, presence of liquid water in a snowpack is known to decrease the radar wave velocity, which for a typical snowpack with 5% (by volume) liquid water can lead to an overestimation of SWE by about 20%. It would therefore be beneficial if radar measurements could also be used to determine snow wetness. Our approach is to use radar wave attenuation in the snowpack, which depends on electrical properties of snow (permittivity and conductivity) which in turn depend on snow wetness. The relationship between radar wave attenuation and these electrical properties can be derived theoretically, while the relationship between electrical permittivity and snow wetness follows a known empirical formula, which also includes snow density. Snow wetness can therefore be determined from radar wave attenuation if the relationship between electrical conductivity and snow wetness is also known. In a laboratory test, three sets of measurements were made on initially dry 1 m thick snowpacks. Snow wetness was controlled by stepwise addition of water between radar measurements, and a linear relationship between electrical conductivity and snow wetness was established.

Ground Penetrating Radar Radargram Filter using Singularity Expansion Method

2021 ◽  
Vol 35 (11) ◽  
pp. 1437-1438
Author(s):  
Eder Ruiz ◽  
Daniel Chaparro-Arce ◽  
John Pantoja ◽  
Felix Vega ◽  
Chaouki Kasmiv ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Expansion Method ◽  
Processing Technique ◽  
Sar Image ◽  
Metallic Material ◽  
Buried Objects ◽  
Ground Penetration Radar ◽  
Ground Penetrating

In this paper, the singularity expansion method (SEM) is used to improve the signal-to-clutter ratio of radargrams obtained with a ground penetration radar (GPR). SEM allows to select the poles of the GPR signals corresponding to unwanted signals, clutter, and also reflections of specific buried objects. A highly reflective metallic material was used to assess the use of SEM as a tool to eliminate unwanted reflections and signals produced by a GPR. Selected clutter poles are eliminated from each frame of the SAR image in order to keep only desired poles for analysis. Finally, the reconstructed radargram obtained applying SEM is compared with the image obtained using a well-known processing technique. Results show that the proposed technique can be used to straightforwardly remove undesired signals measured with GPRs.

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.

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