<title>Multispectral data fusion using neural networks</title>

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.