Fuzzy modelling of vegetation from remotely sensed imagery

Remote sensing has considerable potential for vegetation mapping. The model of vegetation distribution represented in an image classification, however, may not always be appropriate as the algorithms typically used give a 'hard' class allocation. Here the output of three classification techniques, a maximum likelihood, artificial neural network and fuzzy sets classification, are softened and shown to be able to reflect the class composition of image pixels and so be able to provide a better representation of some vegetation from remotely sensed imagery.