Several studies have revealed the potential of artificial neural network computational models (ANN) in processing remotely sensed imagery:
- Competitive accuracy when compared with statistical techniques like Bayesian methods or support vector machines.
- No prior knowledge necessary about the statistical distribution of the classification classes in the source data.
- Well suited for integrating multi-source, conceptually-varied data.
- Their parallel data processing capability make them fast and robust.
Neumapper implements in the same environment the various stages in the generation of an ANN for automatic pixel-based image classification:
- Definition of the network topology.
- Generation of training data.
- Training of the network.
- Classification of an image using the trained network.