We introduce two statistical models designed to detect discrete objects in sidescan SONAR which consider complimetary approaches to the problem. The first considers a complex textural model for the objects and implements detection through a dual hypothesis on texture class presence, while the second implements a complex Gibbs field model of the image and utilises prior knowledge of typical object morphologies to support its detection rate. The models are demonstrated on examples of different seabed sediments and object types, and are shown to be reliable in operation. The common theme of the two models is use of spatial context in analysis, which, we argue, is a very powerful technique for improving the flexibility and reliability of any analysis system. Keywords: Spatial Models, Multidimensional Log-normal distribution, Bayesian Image Reconstruction, Gibbs Fields, Markov Chain Monte Carlo techniques 1. INTRODUCTION We consider in this paper the problem of detection of discrete obje..
