A Simple Neural Network Contextual Classifier

Jens Tidemann, Allan Aasbjerg Nielsen

AbstractIn this paper we describe a neural network used to make a simple contextual classifier using a two layer feed-forward network. The best number of hidden units is chosen by training a network with too many hidden units. We then prune the network using Optimal Brain Damage (OBD). The pruned networks have a better generalisation error because they only have the weights that reflect the structure of the data and not the noise. We study the possibility of using a Network Information Criterion (NIC) to decide when to stop pruning. When we use NIC we ean estimate the test error of a network without using an independent validation set. As a case study we use a four band Landsat-2 Multispectral Scanner (MSS) image from southern Greenland. To classify a pixel in the non-contextual case we use the four variables from the MSS bands only. In the simple contextual case we augment the feature vector with the four mean values of the MSS bands from the four nearest neighbours. We notice an increase in the number of correct classified pixels when using the contextual classifier. AIso, the application of the simple contextual classifier gives a small overall increase in the posterior probability.
TypeBook [Chapter]
Book titleNeurocomputation in Remote Sensing Data Analysis
EditorsI. Kanellopoulos, G.G. Wilkinson, F. Roli and J. Austin
Year1997    pp. 186-193
AddressBerlin, Germany
ISBN / ISSN3-540-63316-2
NoteInvited contribution
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Image Analysis & Computer Graphics