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JACIII Vol.4 No.4 pp. 240-245
doi: 10.20965/jaciii.2000.p0240
(2000)

Paper:

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

Received:
May 12, 2000
Accepted:
July 20, 2000
Published:
July 20, 2000
Keywords:
ARTMAP, Fuzzy ARTMAP, GAUSSIAN ARTMAP, Remote sensing
Abstract

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.

Cite this article as:
Norbert Kopco, Peter Sincak, and Stanislav Kaleta, “ARTMAP Neural Networks for Multispectral Image Classification,” J. Adv. Comput. Intell. Intell. Inform., Vol.4, No.4, pp. 240-245, 2000.
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Last updated on May. 04, 2021