Side-Scan Sonar Image Synthesis Based on Generative Adversarial Network for Images in Multiple Frequencies
Yifan Jiang, Bonhwa Ku, Wan‐Jin Kim, Hanseok Ko
Abstract
The side-scan sonar (SSS) is a critical sensor device used to explore underwater environments in the deep sea. Gathering SSS data, however, is an expensive and time-consuming task because it requires sensor towing and involves complicated field operations. Recently, deep learning has been making advances rapidly in the field of computer vision. Benefiting from this development, generative adversarial networks (GANs) have been demonstrated to produce realistic synthetic data of various types, including images and acoustics signals. In this letter, we propose a GAN-based semantic image synthesis model based on GAN that can generate high-quality SSS images at a low cost in less time. We evaluate the proposed model using both shallow and deep water SSS data sets that include a diverse range of imaging conditions. such as high and low sonar operating frequencies and different landscapes. The experimental results show that the proposed method can effectively generate synthesized SSS data characterized by the shape and style of real data, thereby demonstrating its promising potential for SSS data augmentation in diverse SSS relevant machine learning tasks.