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Proceedings Paper

High-speed neural network filtering of hyperspectral data
Author(s): Tom G. Thomas; Radhakrishna M. Nalakurthi
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Paper Abstract

Hyperspectral image processing techniques are utilized for a variety of applications from geological surveys to detection of camouflaged enemy vehicles. One of the persistent problems is that huge amounts of data must be processed, since a hundred or more frequency bands of spectral information can make up a typical hyperspectral image cube. If real time processing is necessary, as in target tracking or identification, some means of selecting which bands are relevant to the image and which bands can be safely ignored is desirable. We propose a fast, easily trainable neural network filter architecture that can rapidly screen a hyperspectral image cube in near real time. A bank of filters, operating in parallel, is used to screen an image for suspected targets. Performance on simulated and real images is compared to existing recognition techniques and results in considerable reduction in overall image processing time and greater accuracy.

Paper Details

Date Published: 23 October 2003
PDF: 10 pages
Proc. SPIE 5202, Optical Information Systems, (23 October 2003); doi: 10.1117/12.505931
Show Author Affiliations
Tom G. Thomas, Univ. of South Alabama (United States)
Radhakrishna M. Nalakurthi, Univ. of South Alabama (United States)

Published in SPIE Proceedings Vol. 5202:
Optical Information Systems
Bahram Javidi; Demetri Psaltis, Editor(s)

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