Radiative transfer algorithms in combination with empirical formulae have been the most popular approach to the analysis of ocean primary productivity from remotely sensed images of the Earth. These methods fully rely on the limited amounts of ground truth data available and assumptions regarding how sensor, Earth's surface and atmospheric properties influence the radiation captured in different ranges of the electromagnetic spectrum. As these assumptions are restraining, multi-spectral and fusion techniques based on the application of unsupervised neural networks can contribute to the improvement in ocean colour studies and enable analysis of complex water types. This chapter presents the application of a hierarchy of self-organizing feature maps to clustering and differentiation of oceanic waters. The practical studies are performed on imagery captured all over the Pacific Ocean by the Ocean Colour and Temperature Scanner on board the Japanese satellite ADEOS.
