Seabed segmentation in synthetic aperture sonar images

摘要

A synthetic aperture sonar (SAS) image segmentation algorithm using features from a parameterized intensity image autocorrelation function (ACF) is presented. A modification over previous parameterized ACF models that better characterizes periodic or rippled seabed textures is presented and discussed. An unsupervised multiclass k-means segmentation algorithm is proposed and tested against a set of labeled SAS images. Segmentation results using the various models are compared against sand, rock, and rippled seabed environments