WAEGAN: A GANs-Based Data Augmentation Method for GPR Data
Di Zhao, Guinan Guo, Zhi-Kang Ni, Jun Pan, Kun Yan, Guangyou Fang
Abstract
Ground-penetrating radar (GPR) has been widely used to detect subsurface objects. In recent years, deep learning techniques have achieved significant success in image recognition, which has potential implications for interpreting GPR data. However, reliable training of deep learning models requires massive amounts of labeled data, which can be difficult to obtain due to the high costs of data acquisition and field validation. This letter proposes a GPR data augmentation method based on generative adversarial networks (GANs) - Wasserstein GANs (WAEGAN). This proposed method utilizes a GANs model with an encoder E, a joint generator G, and a discriminator D to generate data. A pre-trained classifier C imposes target category constraints on the generated data, while a Wasserstein loss function is employed to stabilize the training process.The performance of the proposed method is evaluated in terms of the validity, diversity, and impact on the classifier performance of the generated fake GPR data. The experimental results verify the superiority of the proposed method in simultaneously generating multiple target categories and generating GPR data that conforms to reality.