This paper evaluates the classification accuracy of three neural network classifiers on a satellite
image-based pattern classification problem. The neural network classifiers used include two types
of the Multi-Layer-Perceptron (MLP) and the Radial Basis Function Network. A normal
(conventional) classifier is used as a benchmark to evaluate the performance of neural network
classifiers. The satellite image consists of 2,460 pixels selected from a section (270 x 360) of a
Landsat-5 TM scene from the city of Vienna and its northern surroundings. In addition to
evaluation of classification accuracy, the neural classifiers are analysed for generalization capability
and stability of results. Best overall results (in terms of accuracy and convergence time) are
provided by the MLP-1 classifier with weight elimination. It has a small number of parameters and
requires no problem-specific system of initial weight values. Its in-sample classification error is
7.87% and its out-of-sample classification error is 10.24% for the problem at hand. Four classes of
simulations serve to illustrate the properties of the classifier in general and the stability of the result
with respect to control parameters, and on the training time, the gradient descent control term,
initial parameter conditions, and different training and testing sets. (authors' abstract) / Series: Discussion Papers of the Institute for Economic Geography and GIScience
| Identifer | oai:union.ndltd.org:VIENNA/oai:epub.wu-wien.ac.at:4184 |
| Date | 05 1900 |
| Creators | Fischer, Manfred M., Gopal, Sucharita, Staufer-Steinnocher, Petra, Steinocher, Klaus |
| Publisher | WU Vienna University of Economics and Business |
| Source Sets | Wirtschaftsuniversität Wien |
| Language | English |
| Detected Language | English |
| Type | Paper, NonPeerReviewed |
| Format | application/pdf |
| Relation | http://epub.wu.ac.at/4184/ |
Page generated in 0.0022 seconds