A 3D GAN Architecture for Volumetric Synthetic Aperture Sonar

Greg D. Vetaw, Albert Reed, Daniel C. Brown, Suren Jayasuriya

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Synthetic aperture sonar (SAS) is used extensively in underwater imaging for visualizing the seafloor and objects present on it. However, processing SAS images can be time-consuming and tedious, with machine learning techniques being ineffective due to the lack of available data. In particular, automated target recognition (ATR) with 3D SAS data for machine learning is challenging in many ways due to the complexity with working with 3D volumetric data. Recently, researchers have introduced generative adversarial networks (GANs) to help perform 2D SAS image generation for data augmentation. Following this line of work in this paper, we introduce a 3D-GAN architecture to generate photorealistic 3D SAS data which matches the fidelity of real data. In particular, we discuss novel latent space sampling and normalization to help 3D GANs overcome mode collapse for generating volumetric SAS information. Experimental results are shown on real 3D SAS data, showing the potential of using 3D GANs for dataset augmentation in the future.

Original languageEnglish (US)
Title of host publicationOCEANS 2021
Subtitle of host publicationSan Diego - Porto
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780692935590
StatePublished - 2021
EventOCEANS 2021: San Diego - Porto - San Diego, United States
Duration: Sep 20 2021Sep 23 2021

Publication series

NameOceans Conference Record (IEEE)
ISSN (Print)0197-7385


ConferenceOCEANS 2021: San Diego - Porto
Country/TerritoryUnited States
CitySan Diego

ASJC Scopus subject areas

  • Oceanography


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