Publications

ConvNet Fine-Tuning Investigation for GPR Images Classification

Elsaadouny, M. and Barowski, J. and Rolfes, I.

2021 34TH GENERAL ASSEMBLY AND SCIENTIFIC SYMPOSIUM OF THE INTERNATIONAL UNION OF RADIO SCIENCE, URSI GASS 2021
Volume: Pages:
DOI: 10.23919/URSIGASS51995.2021.9560298
Published: 2021

Abstract
Deep learning has been widely implemented as a new classification platform during the past few years. One of the main problems facing deep learning is the problem of data dependency as it requires a very large amount of data for training. Therefore, transfer learning (TL) has been introduced as a solution to this problem. This study focuses on the fine-tuning strategy of the transfer learning and how it can be implemented to classify the ground-penetrating radar (GPR) images. The GPR data has been collected and processed using the matched filter algorithm and further clutter reduction techniques. The resultant GPR images compromises of a limited number of samples, therefore, the deployed convolutional neural network (ConvNet) has been trained first using another larger dataset, then fine-tuned using the GPR dataset. The obtained results are promising and show a high degree of precision and accuracy compared to previously conducted researches. © 2021 URSI.

« back