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Proceedings Paper

Multispectral data fusion using neural networks
Author(s): Richard Haberstroh; Ivan Kadar
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Paper Abstract

This paper describes an investigation of the discrimination of heavy and light space objects based on infrared (IR) multi-spectral surveillance data. A time-sequence model of sensor measurements was used to produce test data, upon which the signal processing and discrimination algorithms were to be tested. The signal processing algorithms are based primarily on the estimation of the sinusoid that modulates the signal in the three IR bands, this frequency being one useful discrimination feature. Both frequency domain and novel-time domain techniques were investigated. The time domain technique employs binary median filtering of the original time sequence of measurements with its quadratically modeled trend removed. A second feature for discrimination is also proposed, based upon the quality of fit of the estimated sinusoid to the original time sequence. This combination of features from multiple IR bands was fused using the back-propagation neural network (BPNN) and the polynomial neural network (PNN), which were shown to provide excellent discrimination of the two target classes of interest.

Paper Details

Date Published: 3 September 1993
PDF: 11 pages
Proc. SPIE 1955, Signal Processing, Sensor Fusion, and Target Recognition II, (3 September 1993); doi: 10.1117/12.155002
Show Author Affiliations
Richard Haberstroh, Grumman Corp. (United States)
Ivan Kadar, Grumman Corp. (United States)

Published in SPIE Proceedings Vol. 1955:
Signal Processing, Sensor Fusion, and Target Recognition II
Ivan Kadar; Vibeke Libby, Editor(s)

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