Comparing neural network classifiers and feature selection for target detection in hyperspectral imagery

Joe R. Brown; Edward E. DeRouin

doi:10.1117/12.139993

16 September 1992 Comparing neural network classifiers and feature selection for target detection in hyperspectral imagery

Joe R. Brown, Edward E. DeRouin

Author Affiliations +

Proceedings Volume 1709, Applications of Artificial Neural Networks III; (1992) https://doi.org/10.1117/12.139993
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

This paper summarizes a research effort to explore the use of neural networks in processing hyperspectral imagery for the purpose of target detection and feature selection in an automatic target detection (ATR) scenario. Images containing 32 spectral bands in the 2.0 to 2.5 micrometers infrared range and with co-registered pixels were used to train and test a backpropagation neural network for detection of ground targets. The dimensionality of the original feature set was reduced using two methods, Karhunen-Loeve and Ruck's saliency technique. The results for the two feature selection techniques are compared using classifier performance as a metric. Finally, a neural network chip (ETANN) was used to test the feasibility of hardware implementation of the fusion processing.

Citation Download Citation

Joe R. Brown and Edward E. DeRouin "Comparing neural network classifiers and feature selection for target detection in hyperspectral imagery", Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); https://doi.org/10.1117/12.139993

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