TABLE OF CONTENTS 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, provided 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 surfactants 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 likelihood 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 themmore 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; we commence this chapter with a review of its pertinent properties, which are discussed in more detail in Appendix 1.
So far we have considered the analysis of un-correlated data sets, whose joint pdf can be decomposed into a product of those of the individual data values (e.g. 6.26). In general the extension of these arguments...
