Towards automated performance assessment in synthetic aperture sonar

Synthetic aperture sonar (SAS) is a signal processing technique which increases the along-track resolution in sonar images by coherent combination of collected pings. Successful SAS processing is dependent on accurate navigation, knowledge about the ocean environment, and accurate bathymetric maps. In general, the quality of SAS images will vary dependent on these factors. We propose a model for automated prediction and assessment of SAS performance or image quality. The intended use is to give an estimate of the local quality of data in a particular SAS image. The model consists of modules for generalized signal to noise, geometric resolution, radiometric resolution (value accuracy) and image sharpness. In this paper, specifically focus on image sharpness. We show example data sets collected by an interferometric SAS on an autonomous underwater vehicle.