Synthetic Aperture Sonar Image Segmentation Using Adaptive, Learned Beam Steering

Synthetic aperture sonar (SAS) produces high resolution imagery of the seafloor. Automatic segmentation of this imagery is of interest to a variety of ocean-studying communities. In this work, we propose a novel supervised image segmentation method using adaptive, learned beam steering in the k-space domain in order to exploit the aspect dependent information available in the single-look-complex (SLC) SAS image which is overlooked in current SAS segmentation methods. Our proposal is a deep neural network architecture designed around the domain-specific beam steering geometry conveyed by the k-space domain. Results on a small real-world SAS dataset demonstrate that our proposal gives improved results for a variety of seafloor classes over a state-of-the-art (SOTA) U-Net.