Title :
Hyperspectral data fusion for target detection using neural networks
Author :
DeRouin, Ed ; Beck, Hal ; Brown, Joe R. ; Bergondy, Dan ; Archer, Sue
Author_Institution :
Martin Marietta Electron., Inf. & Missile Syst., Orlando, FL, USA
Abstract :
Summary form only given. A research effort was carried out to explore the use of neural networks in processing hyperspectral imagery for target detection and classification. Pixel registered imagery containing 32 spectral bands in the 2.0 to 2.5 μm range was used to train and test a backpropagation neural net for detection of camouflaged targets. Because of the high degree of correlation between features, the dimensionality of the feature set was reduced using a Karhunen-Loeve expansion
Keywords :
computerised pattern recognition; military computing; neural nets; spectral analysis; Karhunen-Loeve expansion; backpropagation neural net; camouflaged targets; classification; dimensionality; feature set; hyperspectral imagery; neural networks; research effort; spectral bands; target detection; Asphalt; Hyperspectral imaging; Hyperspectral sensors; Image sensors; Missiles; Neural networks; Object detection; Predictive models; Sensor systems; Target recognition;
Conference_Titel :
Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-0164-1
DOI :
10.1109/IJCNN.1991.155505
