Evaluation of Neural Pattern Classifiers for a Remote Sensing Application

Fischer, Manfred M. and Gopal, Sucharita and Staufer-Steinnocher, Petra and Steinocher, Klaus (1995) Evaluation of Neural Pattern Classifiers for a Remote Sensing Application. Discussion Papers of the Institute for Economic Geography and GIScience, 46/95. WU Vienna University of Economics and Business, Vienna.


Download (12MB)


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)

Item Type: Paper
Keywords: Neural Classifiers / Classification of Multispectral Image Data / Pixel-by-Pixel Classification / Backpropagation / Sensitivity Analysis
Divisions: Departments > Sozioökonomie > Wirtschaftsgeographie und Geoinformatik > Fischer
Depositing User: ePub Administrator
Date Deposited: 30 Jun 2014 09:58
Last Modified: 22 Dec 2015 11:35
URI: https://epub.wu.ac.at/id/eprint/4184


View Item View Item


Downloads per month over past year

View more statistics