Abstract
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.
Highlights
Ground penetrating radar (GPR) is a type of electromagnetic technique for the detection of subsurface targets
As underground object detection is always involved in these fields, the subsurface target classification of the GPR becomes a popular topic in the geophysical field
Ukaegbu et al combined GPR and a gamma ray detector to estimate the nonintrusive depth of buried radioactive wastes [1]; Wentao Li et al applied a randomized Hough transform to achieve the automatic recognition of tree roots [2]; Byeongjin Park et al used instantaneous phase analysis of GPR data to perform underground object classification [3]; Xuan Feng et al applied migration for the detection of underground objects [4,5,6]
Summary
Ground penetrating radar (GPR) is a type of electromagnetic technique for the detection of subsurface targets. H-Alpha decomposition is a method based on the Kennaugh matrix, which was proposed by Cloude and Pottier in 1997 [14] This method has been applied widely to synthetic aperture radar (SAR), as well as to target classification in GPR [5] in recent years. Xavier Núñez-Nieto et al applied logistic regression and neural network (NN) techniques to automated landmine and UXO detection [26]; Tao Liu et al used neural networks to inverse GPR data [27]; Haoqiu Zhou et al combined full polarimetric GPR and support vector machine (SVM) data for subsurface target classification [28]; Minghe Zhang et al used freeman decomposition and random forest (RF) to perform underground object detection [29]. Remote Sens. 2019, 11, 405 depths, diameters, and materials; subsequently, classical H-Alpha classification and support vector machine (SVM) analysis are performed to compare with the PCSP method
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