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Open AccessArticle

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

1
College of Geo-Exploration Science and Technology, Jilin University, No.938 Xi MinZhu Street, Changchun 130026, China
2
College of Earth Sciences, Jilin University, Changchun 130061, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(4), 405; https://doi.org/10.3390/rs11040405
Received: 21 December 2018 / Revised: 13 February 2019 / Accepted: 14 February 2019 / Published: 17 February 2019
(This article belongs to the Special Issue Recent Progress in Ground Penetrating Radar Remote Sensing)
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. View Full-Text
Keywords: full polarimetric GPR; machine learning (ML); classification; particle center supported plane (PCSP); particle swarm optimization (PSO); H-Alpha decomposition full polarimetric GPR; machine learning (ML); classification; particle center supported plane (PCSP); particle swarm optimization (PSO); H-Alpha decomposition
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MDPI and ACS Style

Feng, X.; Zhou, H.; Liu, C.; Zhang, Y.; Liang, W.; Nilot, E.; Zhang, M.; Dong, Z. Particle Center Supported Plane for Subsurface Target Classification based on Full Polarimetric Ground Penetrating Radar. Remote Sens. 2019, 11, 405. https://doi.org/10.3390/rs11040405

AMA Style

Feng X, Zhou H, Liu C, Zhang Y, Liang W, Nilot E, Zhang M, Dong Z. Particle Center Supported Plane for Subsurface Target Classification based on Full Polarimetric Ground Penetrating Radar. Remote Sensing. 2019; 11(4):405. https://doi.org/10.3390/rs11040405

Chicago/Turabian Style

Feng, Xuan; Zhou, Haoqiu; Liu, Cai; Zhang, Yan; Liang, Wenjing; Nilot, Enhedelihai; Zhang, Minghe; Dong, Zejun. 2019. "Particle Center Supported Plane for Subsurface Target Classification based on Full Polarimetric Ground Penetrating Radar" Remote Sens. 11, no. 4: 405. https://doi.org/10.3390/rs11040405

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