An in-air synthetic aperture sonar dataset of target scattering in environments of varying complexity.

This paper describes a synthetic aperture sonar (SAS) dataset collected in-air consisting of four types of targets in four environments of different complexity. The in-air laboratory based experiments produced data with a level of fidelity and ground truth accuracy that is not easily attainable in data collected underwater. The range of complexity, high level of data fidelity, and accurate ground truth provides a rich dataset with acoustic features on multiple scales.

Bayesian data fusion of multiview synthetic aperture sonar imagery for seabed classification.

A Bayesian data fusion approach for seabed classification using multiview synthetic aperture sonar (SAS) imagery is proposed. The principled approach exploits all available information and results in probabilistic predictions. Each data point, corresponding to a unique 10 m x 10 m area of seabed, is represented by a vector of wavelet-based features.