ARTMAP Neural Networks for Multispectral Image Classification
Norbert Kopco*, Peter Sincak** and Stanislav Kaleta**
*Department of Cognitive and Neural Systems, Boston University, 677 Beacon St., Boston, MA 02215, the U.S.A. And CIG, KKUI, FEI TU Kosice, Letna 9, 04001 Kosice, Slovak Rep.
**Computational Intelligence Group, Department of Cybernetics and AI Faculty of EE and Informatics, Technical University, Letna 9, 04001 Kosice, Slovak Republic
This paper presents an analysis of performance of several types of the ARTMAP neural network. The performance of the networks is analyzed in the task of classification of satellite images obtained by remote sensing. The analysis is concentrated on the dependence of classification accuracy on the difference in cluster type preferably identified by each of the classifiers. Three types of ARTMAP classifier are compared: fuzzy ARTMAP, Gaussian ARTMAP, and Extended Gaussian ARTMAP The main difference among these classifiers is in the way they determine/represent individual clusters in feature space. Best results are obtained for Extended Gaussian ARTMAP, a modification of the Gaussian ARTMAP neural network that preferably identifies Gaussian-distributed clusters.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 International License.