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http://dx.doi.org/10.36498/kbigdt.2021.6.1.197

A Study on GPR Image Classification by Semi-supervised Learning with CNN  

Kim, Hye-Mee (부산대학교 산업공학과 산업데이터공학융합전공)
Bae, Hye-Rim (부산대학교 산업공학과 산업데이터공학융합전공)
Publication Information
The Journal of Bigdata / v.6, no.1, 2021 , pp. 197-206 More about this Journal
Abstract
GPR data is used for underground exploration. The data gathered are interpreted by experts based on experience as the underground facilities often reflect GPR. In addition, GPR data are different in the noise and characteristics of the data depending on the equipment, environment, etc. This often results in insufficient data with accurate labels. Generally, a large amount of training data have to be obtained to apply CNN models that exhibit high performance in image classification problems. However, due to the characteristics of GPR data, it makes difficult to obtain sufficient data. Finally, this makes neural networks unable to learn based on general supervised learning methods. This paper proposes an image classification method considering data characteristics to ensure that the accuracy of each label is similar. The proposed method is based on semi-supervised learning, and the image is classified using clustering techniques after extracting the feature values of the image from the neural network. This method can be utilized not only when the amount of the labeled data is insufficient, but also when labels that depend on the data are not highly reliable.
Keywords
GPR; Image classification; CNN; Semi-supervised learning; Image clustering;
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