Introduction

Abstract

Neural network or connectionist algorithms have made an enormous impact in the field of signal processing over the last decade. Although few would regard them as the perfect answer to all pattern recognition problems, there is little doubt that they have contributed significantly to the solution of some of the most difficult ones. Trainable networks of primitive processing elements have been shown to be capable of describing and modelling systems of great complexity without the necessity of building parameterised statistical descriptions. Such capability has led to considerable and constantly growing interest from the remote sensing community. From early beginnings in the late 1980’s, neural network algorithms are now being explored for a wide range of uses in Earth observation. In many cases these uses are still experimental or at the pre-operational stage.