In the previous chapter we considered how we might best detect small, localised targets in a background of sea clutter. The identification of the likelihood ratio as an optimum discriminant, and of its more practically useful approximations, pro vided us with a unifying framework for this discussion. However, small target returns are not the only features of interest in maritime radar imagery. Large-scale correlated structures arising from surface currents, ship wakes, the presence of sur factants and other sources can frequently be discerned and are a valuable source of information in many circumstances. In this chapter we will discuss how such ocean surface features might best be enhanced and detected. Once again the like lihood ratio concept is a very useful guiding principle, which leads us to methods that enable us to both enhance these features and exploit our prior knowledge of their structure to detect them more effectively. So, paradoxically, a discussion of the processing of images that are frequently interpreted and assessed in qualitative terms, will involve us in a fair amount of detailed formal analysis. Much of this will be based on the multivariate Gaussian distribution.

Inspec keywords: radar clutter; radar imaging; feature extraction; oceanographic techniques; Gaussian distribution

Other keywords: image processing; maritime radar imagery; scattering; Gaussian distribution; imaging ocean surface feature; large-scale correlated structure; surface current; sea clutter; feature detection; radar performance; ship wake; surfactant; likelihood ratio identification; K distribution

Subjects: Radar equipment, systems and applications; Radar theory; Optical, image and video signal processing; Oceanographic and hydrological techniques and equipment