A Parametric Model for Characterizing Seabed Textures in Synthetic Aperture Sonar Images

Abstract

High-resolution synthetic aperture sonar (SAS) systems yield finely detailed images of sea bottom environments. SAS image texture models must be capable of representing a wide variety of sea bottom environments including sand ripples, coral or rock formations, and flat hardpack. In this paper, a parameterized model for SAS image textures is derived from the autocorrelation functions (ACFs) of the SAS imaging point spread function (PSF) and the ACF of the seabed texture sonar cross section (SCS). The proposed texture mixture model is analytically tractable and parameterized by component mixing parameters, mixture component correlation lengths, the single-point intensity image statistical shape parameter, and the rotation of the ACF mixture components in the 2-D imaging plane. An iterative parameter estimation algorithm based on the expectation-maximization (EM) algorithm for truncated data is presented and tested against various synthetic and real SAS image textures. The performance of the algorithm is compared and discussed for synthetically generated data across various image sizes and texture characteristics. The model fit is also compared against a small set of real SAS survey images and is shown to accurately fit the imaging PSF and seabed SCS ACF for these textures of interest.