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16 September 1992 Comparing neural network classifiers and feature selection for target detection in hyperspectral imagery
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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.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Joe R. Brown and Edward E. DeRouin "Comparing neural network classifiers and feature selection for target detection in hyperspectral imagery", Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); https://doi.org/10.1117/12.139993
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