Comparative performance of artificial neural networks and conventional methods for multispectral image fusion

Joseph H. Kagel

doi:10.1117/12.140002

16 September 1992 Comparative performance of artificial neural networks and conventional methods for multispectral image fusion

Joseph H. Kagel

Author Affiliations +

Proceedings Volume 1709, Applications of Artificial Neural Networks III; (1992) https://doi.org/10.1117/12.140002
Event: Aerospace Sensing, 1992, Orlando, FL, United States

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.

Citation Download Citation

Joseph H. Kagel "Comparative performance of artificial neural networks and conventional methods for multispectral image fusion", Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); https://doi.org/10.1117/12.140002

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available