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Proceedings Paper

Comparing neural network classifiers and feature selection for target detection in hyperspectral imagery
Author(s): Joe R. Brown; Edward E. DeRouin
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Paper 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.

Paper Details

Date Published: 16 September 1992
PDF: 7 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.139993
Show Author Affiliations
Joe R. Brown, Microelectronics and Computer Technology Corp. (United States)
Edward E. DeRouin, Martin Marietta Electronic Systems (United States)


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

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