Feedforward neural networks with multilevel hidden neurons for remotely sensed image classification

Artificial neural network has been used as a powerful tool for pattern classification. However, it is difficult to train when the data exhibit non-sparse or overlapping pattern classes which is often the case in practical applications. In this paper, we introduce the feedforward neural network with the hidden layer consisting Of multilevel neurons. The convergence property of one-layer neural net work with multilevel neurons is proved. The new feedforward model is inherently capable of fuzzy pattern classification, of non-sparse or overlapping pattern classes. As an application, we apply the network for the classification of LANDSAT TM data. The results show that this approach produces better results compared with conventional neural networks.