Application of Freeman decomposition to full polarimetric GPR for improving subsurface target classification

Feng, Xuan; Liang, Wei; Liu, Cai; Nilot, Enhedelihai; Zhang, Minghe; Liang, Shuaishuai

doi:10.1016/j.sigpro.2016.07.030

Cited by 20 publications

(11 citation statements)

References 21 publications

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“…Subsequently, we can obtain a 3×3 covariance matrix [C] [5], [22], [25], [38]: The [C] is considered to be composed of three matrices which can be represented as follows [5], [22], [25], [38]:…”

Section: B the Freeman Attributesmentioning

confidence: 99%

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Multiparameter Adaptive Target Classification Using Full-Polarimetric GPR: A Novel Approach to Landmine Detection

Zhou

Feng

Dong

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Full-polarimetric ground penetrating radar (FP-GPR) can measure the ability of an object to change the polarization of electromagnetic (EM) waves. Compared to the traditional GPR, it has a stronger capability to identify underground objects. In recent years, a series of polarization decomposition methods have been applied to the FP-GPR data processing to obtain the polarimetric attributes and enhance the capability of targets identification. Different polarimetric attributes characterize different features of a target but there is still no effective way to integrate these attributes and take their respective advantages for target classification. In this study, we propose a particle center Adaboost (PCAD) method and achieve the multi-parameter adaptive target identification. The experimental results indicate that the PCAD method can automatically select suitable parameters during the training process for different targets. Compared to the single-parameter classification and the Adaboost methods based on the traditional average and Bagging method, the PCAD method presents higher correct rates in classification. Finally, the proposed method is applied to landmine detection. The results demonstrate that the landmine is a composite scatterer that can generate surface scattering signals on its surface and dipole and volume scattering signals from its interior; based on the color-coded 2D image by PCAD, we can distinguish landmines from other targets.

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Section: B the Freeman Attributesmentioning

confidence: 99%

“…where [Cs], [Cd], and [Cv] correspond to the covariance matrices of the surface-like scattering, the double-bounce scattering, and the volume scattering, respectively [5]. fs, fd, and fv denote the energy of the SVV signals for the three types of scatterings, respectively.…”

Section: B the Freeman Attributesmentioning

confidence: 99%

Multiparameter Adaptive Target Classification Using Full-Polarimetric GPR: A Novel Approach to Landmine Detection

Zhou

Feng

Dong

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In full polarimetric GPR, the Sinclair matrix [S] is applied to describe the nature of target scattering. The [S] matrix [13] can be represented as follows:…”

Section: H-alpha Decompositionmentioning

confidence: 99%

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

et al. 2019

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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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“…GPR usually utilise electromagnetic wave in the VHF-UHF region of the electromagnetic spectrum where the frequency is easy to compromise [16][17][18][19]. For the purpose of penetrating deeper it is suggested to use the lowest possible frequency because low frequencies give reasonably high penetration depths into the earth with lesser resolution.…”

Section: Objectivesmentioning

confidence: 99%

Estimation of different subsurface materials depth using Ground Penetrating Radar

Ghazali¹

2019

IJATCSE

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This paper present the technique to compare the depth of pipe measured with Ground Penetrating Radar (GPR) to the actual value determine during pipe installation. In this study six different subsurface materials were used to bury the pipes meant to evaluate the impact of these materials toward the depths obtained from reflected wave. Estimation of depth for buried object required an understanding of ground-penetrating radar (GPR) functioning that include the concept of pulse, wavelength, strength of the energy (amplitude), travelling time as well as reflection of electromagnetic wave. However, there is a considerable lack of the study on the accuracy depth using GPR. This study is attempted to address the knowledge deficit by buring pipe at specific depth and measure it with GPR to estimate depth. Despite increasing application, little consideration has been given to the accuracy of the depth. This study examines the accuracy of the depth by comparing the actual depth with what measured by GPR. Corresponding GPR and manual measurement of the depth were carried out at the site specially prepared for this study. There are two metals and two plastic pipes running through the material at the depth 0.5m and 1m respectively where a few pieces of plywood are used as the separator between them GPR utilises propagating electromagnetic (EM) that travel through subsurface material from the transmitter in form of pulse. When the energy faces a different dielectric constant on the journey it will reflect back up to the receiver at a specific time window. For this study the 250 MHz frequency is used to capture image of different six different samples of soils and one of it is a ready mix concrete. The 250 MHz GPR data set gives a vertical resolution (0.125 m) with an approximate penetration depth of about 3 m.

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Application of Freeman decomposition to full polarimetric GPR for improving subsurface target classification

Cited by 20 publications

References 21 publications

Multiparameter Adaptive Target Classification Using Full-Polarimetric GPR: A Novel Approach to Landmine Detection

Multiparameter Adaptive Target Classification Using Full-Polarimetric GPR: A Novel Approach to Landmine Detection

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

Estimation of different subsurface materials depth using Ground Penetrating Radar

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