An experimental study on spectral discrimination capability of a backpropagation neural network classifier

Data were generated for two classes in a simulated feature space, with the classes having a varying amount of spectral overlap. It was hypothesized that the backpropagation neural network would be able to distinguish the classes in the situation of no overlap. Our results confirmed that two non-overlapping classes can be discriminated with 100% overall accuracy by the backpropagation neural network. The backpropagation neural network classified the simulated data sets with a significantly higher accuracy than the maximum likelihood or the parallelepiped classifier. When the experiment was repeated using remotely sensed imagery with more complicated spectral overlap among classes (for a land cover classification at Lemeleberg in the eastern Netherlands), the neural network yielded again a significantly higher classification accuracy than the maximum likelihood classifier or the parallelepiped classifier. Differences between the map outputs imply that integrating the different classification algorithms may improve the overall mapping accuracy.