Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/1130
Title: | GPR B scan image analysis with deep learning methods | Authors: | Özkaya, Umut Melgani, Farid Bejiga, Mesay Belete Seyfi, Leventl Donelli, Massimo |
Keywords: | Buried Object Detection Convolutional Neural Network (Cnn) Convolutional Support Vector Machine (Csvm) Ground Penetrating Radar B Scan (Gpr B Scan) Ground-Penetrating Radar Landmine Detection Hyperbolas |
Publisher: | ELSEVIER SCI LTD | Abstract: | 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. | URI: | https://doi.org/10.1016/j.measurement.2020.107770 https://hdl.handle.net/20.500.13091/1130 |
ISSN: | 0263-2241 1873-412X |
Appears in Collections: | Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections |
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