Sonar image modeling for texture discrimination and classification

High-resolution synthetic aperture sonar (SAS) systems yield finely detailed images of sea bed 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 hard pack. In this dissertation a parameterized texture model based on the autocorrelation functions (ACF) of the SAS imaging point spread function and the ACF of the seabed texture sonar cross section (SCS) are derived from realistic scattering assumptions. The proposed texture mixture model is analytically tractable and parameterized by component mixing parameters, mixture component correlation lengths, means, the single-point intensity image statistical shape parameter, and the rotation of the ACF mixture components in the 2-D imaging plane. These parameters provide an intuitive, low-dimension representation of the image texture in terms of its contrast, period, orientation, and shape.;To estimate the various ACF mixture model parameters, an iterative algorithm based on the Expectation Maximization algorithm for truncated data is presented and tested against various synthetic and real SAS image textures. The accuracy of the parameter estimation algorithm is compared and discussed for synthetically generated data across various image sizes and texture characteristics. The use of the Bayesian information criteria (BIC) as an effective model selection metric is demonstrated and discussed. ACF model parameters are also estimated for a small set of real SAS survey images and are shown to accurately fit the imaging point spread function and seabed SCS ACF for these textures of interest.;An unsupervised multi-class k-means segmentation algorithm that uses the features derived from the ACF model is employed to label sand, rock, and ripple textures from a set of real textured SAS images. First, results are compared between increasingly complex intensity ACF models, with the most effective being a four-component model capable of extracting the period of the ripple textures. Later, the results of the four-component ACF segmentation are compared against the performance of the segmentation approach using bi-orthogonal wavelets and Haralick features. In the described experiments, the ACF model features are shown to produce better segmentations than the features based on wavelet coefficients and Haralick features for classifiers of low complexity. (Full text of this dissertation may be available via the University of Florida Libraries web site. Please check http://www.uflib.ufl.edu/etd.html).