Özkaya, UmutMelgani, FaridBejiga, Mesay BeleteSeyfi, LeventlDonelli, Massimo2021-12-132021-12-1320200263-22411873-412Xhttps://doi.org/10.1016/j.measurement.2020.107770https://hdl.handle.net/20.500.13091/1130In 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.eninfo:eu-repo/semantics/closedAccessBuried Object DetectionConvolutional Neural Network (Cnn)Convolutional Support Vector Machine (Csvm)Ground Penetrating Radar B Scan (Gpr B Scan)Ground-Penetrating RadarLandmine DetectionHyperbolasArticle10.1016/j.measurement.2020.1077702-s2.0-85087279795