New target detection algorithms for volumetric synthetic aperture sonar data

A fast algorithm for the automated detection of buried and proud objects in three-dimensional (3-d) volumetric synthetic aperture sonar (SAS) imagery is proposed. The method establishes the positions of underwater targets by finding localized volumes of strong acoustic returns on or within the sediment. The algorithm relies on an important data-normalization step that is grounded in principled physics-based arguments, and it greatly reduces the amount of data that must be passed to a follow-on classification stage. The promise of the approach is demonstrated for man-made objects present in real, measured SAS data cubes collected at multiple aquatic sites by an experimental volumetric sonar system.