Aural based scene understanding for sonar applications

Abstract

Acoustic returns scattered from either surfaces or objects are typically interpreted using image processing techniques such as beamforming and computer vision models such as convolutional neural networks. However, image representations can be burdensome to generate, creating delayed responses in real-time scenarios, and difficult to parse from the scene-understanding perspective. This work investigates direct exploitation of the information content from streaming, near-raw sonar time series data for the purpose of scene characterization. Summary statistics drawn from the sonar performance estimation community and communities that study aural-based perception are used to identify scene characteristics such as flat sand, ripples, gravel beds, or sea grass, in side-looking synthetic aperture sonar (SAS) data. An empirical multi-class classification study is presented that explores the performance of seafloor characterization methods on time series versus imagery.