The first spin-off company of University of Rome Tor Vergata

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.
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A simple and intuitive interface streamlines appropriate network design and effective network training into a painless, real-time iterative process in which, after evaluating the accuracy of the resulting image classification, the user can opt for training the network further with the same or a different pattern set, and eventually adjust its topology. The interface permits separate handling of networks, pattern sets and images, as an example enabling multi-image network training, and classification of multiple images using the very same trained network.

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You can post your comments and questions in the Neumapper forum (ESA Earth Observation Users' Single Sign-On login required).