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Proceedings Paper

Comparative performance of artificial neural networks and conventional methods for multispectral image fusion
Author(s): Joseph H. Kagel
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Paper Abstract

This paper compares the performance of an artificial neural network technique to that of two conventional techniques in fusing (classifying) multispectral imagery. The true classification error rate is estimated by use of the k-fold cross-validation technique for a Bayesian classifier, a binary tree classifier, and a backpropagation neural network. The cascade correlation neural network is also described and its theory of operation is compared to that of the backpropagation neural network.

Paper Details

Date Published: 16 September 1992
PDF: 6 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.140002
Show Author Affiliations
Joseph H. Kagel, McDonnell Douglas Electronic Systems Co. (United States)

Published in SPIE Proceedings Vol. 1709:
Applications of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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