Land-cover mapping in the Arno basin, Italy: multispectral classification and neural networks.

Two Landsat Thematic Mapper (TM) images covering the Arno basin, one of the major watersheds in central Italy, have been classified using neural network techniques. The main advantage in using neural network classifiers is that they do not require any a priori assumptions in the statistical distribution of the class, since they are non-parametric classifiers. Furthermore, the ability of neural networks to learn and adapt to different situations makes them more flexible and capable of recognizing inputs with a higher degree of noise. Different network architectures have been trained and applied, and different levels of discrimination have been tested, i.e. various numbers of target classes. A two-layer feed-forward network with a logsigmoid transfer function gave the best performance. Results show that the recognition of some classes is excellent with neural networks, while for others there are still a large number of pixels incorrectly classified.