Coupling rendering and generative adversarial networks for artificial SAS image generation

Albert Reed, Isaac D. Gerg, John D. McKay, Daniel C. Brown, David P. Williamsk, Suren Jayasuriya

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

9 Scopus citations


There is a growing demand for large-scale Synthetic Aperture Sonar (SAS) datasets. This demand stems from data-driven applications such as Automatic Target Recognition (ATR) [1]-[3], segmentation [4] and oceanographic research of the seafloor, simulation for sensor prototype development and calibration [5], and even potential higher level tasks such as motion estimation [6] and micronavigation [7]. Unfortunately, the acquisition of SAS data is bottlenecked by the costly deployment of SAS imaging systems, and even when data acquisition is possible, the data is often skewed towards containing barren seafloor rather than objects of interest. This skew introduces a data imbalance problem wherein a dataset can have as much as a 1000-to-1 ratio of seafloor background to object-of-interest SAS image chips.

Original languageEnglish (US)
Title of host publicationOCEANS 2019 MTS/IEEE Seattle, OCEANS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780578576183
StatePublished - Oct 2019
Externally publishedYes
Event2019 OCEANS MTS/IEEE Seattle, OCEANS 2019 - Seattle, United States
Duration: Oct 27 2019Oct 31 2019

Publication series

NameOCEANS 2019 MTS/IEEE Seattle, OCEANS 2019


Conference2019 OCEANS MTS/IEEE Seattle, OCEANS 2019
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Automotive Engineering
  • Ocean Engineering
  • Acoustics and Ultrasonics
  • Fluid Flow and Transfer Processes
  • Oceanography


Dive into the research topics of 'Coupling rendering and generative adversarial networks for artificial SAS image generation'. Together they form a unique fingerprint.

Cite this