Spectral Partitioning of Synthetic Aperture Sonar Imagery for Improved ATR

Abstract

A principled physics-based approach for data augmentation with synthetic aperture sonar (SAS) imagery is proposed. The approach is based on partitioning the wavenumber spectrum of the data. The images that result from retaining only specific sectors of spectral content are referred to as “ghosts.” The approach enables the generation of practically infinite mildly correlated images: high enough that key fundamental features of objects persist, but low enough to engender desired data diversity. The ghosts can be used to help train data-hungry convolutional neural networks (CNNs), but they can also be leveraged at inference time to provide a more robust ensemble prediction that also carries with it a measure of uncertainty. Experimental results on an object classification task with real, measured SAS data highlight the benefits of the approach.