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GPR B scan image analysis with deep learning methods
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DOI：10.1016/j.measurement.2020.107770.png)
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Abstract
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In this paper, we propose a Convolutional Support Vector Machine (CSVM) network for the analysis of Ground Penetrating Radar B Scan (GPR B Scan) images. Similar to a Convolutional Neural Network (CNN) architecture, a CSVM is also a cascade of convolution and pooling layers. However, the main difference is that it utilizes linear Support Vector Machine (SVM) based filters to generate feature maps and follows a forward learning strategy. We applied proposed method for the classification of buried objects, shape type and soil type. We used simulated GPR B scan images to train the networks. Proposed method was tested on both simulated and real GPR B scan images. In addition, we conducted a comparative study of the CSVM method with the classical machine learning approaches and pre-trained CNN models. Experimental results show that the proposed method provides an improved classification performance while the computational complexity is low. (C) 2020 Elsevier Ltd. All rights reserved.
Keywords:
Buried object detection
Convolutional Neural Network (CNN)
Convolutional Support Vector Machine (CSVM)
Ground Penetrating Radar B Scan (GPR B Scan)
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