Particle Center Supported Plane for Subsurface Target Classification based on Full Polarimetric Ground Penetrating Radar
| Autor: | Minghe Zhang, Yan Zhang, Enhedelihai Nilot, Zejun Dong, Cai Liu, Xuan Feng, Haoqiu Zhou, Wenjing Liang |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Synthetic aperture radar
full polarimetric GPR 010504 meteorology & atmospheric sciences Computer science Science 0211 other engineering and technologies Polarimetry 02 engineering and technology 01 natural sciences 021101 geological & geomatics engineering 0105 earth and related environmental sciences particle swarm optimization (PSO) business.industry machine learning (ML) H-Alpha decomposition Particle swarm optimization Pattern recognition classification particle center supported plane (PCSP) Sample (graphics) Support vector machine Data set Outlier Ground-penetrating radar General Earth and Planetary Sciences Artificial intelligence business |
| Zdroj: | Remote Sensing, Vol 11, Iss 4, p 405 (2019) Remote Sensing; Volume 11; Issue 4; Pages: 405 |
| ISSN: | 2072-4292 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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