Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/1130
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Özkaya, Umut | - |
dc.contributor.author | Melgani, Farid | - |
dc.contributor.author | Bejiga, Mesay Belete | - |
dc.contributor.author | Seyfi, Leventl | - |
dc.contributor.author | Donelli, Massimo | - |
dc.date.accessioned | 2021-12-13T10:34:43Z | - |
dc.date.available | 2021-12-13T10:34:43Z | - |
dc.date.issued | 2020 | - |
dc.identifier.issn | 0263-2241 | - |
dc.identifier.issn | 1873-412X | - |
dc.identifier.uri | https://doi.org/10.1016/j.measurement.2020.107770 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/1130 | - |
dc.description.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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | ELSEVIER SCI LTD | en_US |
dc.relation.ispartof | MEASUREMENT | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Buried Object Detection | en_US |
dc.subject | Convolutional Neural Network (Cnn) | en_US |
dc.subject | Convolutional Support Vector Machine (Csvm) | en_US |
dc.subject | Ground Penetrating Radar B Scan (Gpr B Scan) | en_US |
dc.subject | Ground-Penetrating Radar | en_US |
dc.subject | Landmine Detection | en_US |
dc.subject | Hyperbolas | en_US |
dc.title | GPR B scan image analysis with deep learning methods | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1016/j.measurement.2020.107770 | - |
dc.identifier.scopus | 2-s2.0-85087279795 | en_US |
dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü | en_US |
dc.authorid | Donelli, Massimo/0000-0001-9477-9243 | - |
dc.authorwosid | Donelli, Massimo/G-3009-2017 | - |
dc.identifier.volume | 165 | en_US |
dc.identifier.wos | WOS:000575758000014 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 57191610477 | - |
dc.authorscopusid | 35613488300 | - |
dc.authorscopusid | 57192697078 | - |
dc.authorscopusid | 36242356400 | - |
dc.authorscopusid | 7006135869 | - |
dc.identifier.scopusquality | - | - |
item.grantfulltext | embargo_20300101 | - |
item.openairetype | Article | - |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | 02.04. Department of Electrical and Electronics Engineering | - |
crisitem.author.dept | 02.04. Department of Electrical and Electronics Engineering | - |
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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1-s2.0-S0263224120303080-main.pdf Until 2030-01-01 | 951.65 kB | Adobe PDF | View/Open Request a copy |
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