Semi-Supervised Triple-GAN With Similarity Constraint for Automatic Underground Object Classification Using Ground Penetrating Radar Data

Abstract

Automatic underground object classification based on deep learning (DL) has been widely used in ground penetrating radar (GPR) fields. However, its excellent performance heavily depends on sufficient labeled training data. In GPR fields, large amounts of labeled data are difficult to obtain due to time-consuming and experience-dependent manual annotation work. To address the issue of limited labeled data, we propose a novel semi-supervised learning (SSL) method for urban-road underground multiclass object classification. It fully utilizes abundant unlabeled data and limited labeled data to enhance classification performance. We applied a variant of the triple-GAN (TGAN) model and modified it by introducing a similarity constraint, which is associated with GPR data geometric features and can help to produce high-quality generated images. Experimental results of laboratory and field data show that it has higher accuracy than representative baseline methods under limited labeled data.