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Proceedings Paper

Application of neural networks to hyperspectral image analysis and interpretation
Author(s): Sylvia S. Shen; Brian D. Horblit
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Paper Abstract

The newest airborne and spaceborne imaging sensors, unlike their predecessors, generate data at tens to hundreds of wavelengths simultaneously. The advent of these imaging spectrometers greatly increases the detail of the information available to the analyst, but the concomitant disadvantages of vastly increased data volume and complexity have stimulated the search for more efficient and accurate hyperspectral data exploitation techniques. This paper describes two neural network models used in conjunction to analyze hyper-spectral image data. The first neural network is based on an unsupervised backward error propagation model. This network generates without supervision low dimensional discriminant feature vectors from the original high dimensional signals. These features are analyzed by the second neural network, which is a layered feed-forward neural network based on statistical principles, namely Parzen window density estimation and Bayes decision rule. This network estimates class-conditional probability density functions using training samples and classifies data points so that the expected risk is minimized. Results of applying these two neural networks to 210-band AVIRIS data are presented.

Paper Details

Date Published: 16 September 1992
PDF: 8 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.139994
Show Author Affiliations
Sylvia S. Shen, Lockheed Palo Alto Research Lab. (United States)
Brian D. Horblit, Lockheed Palo Alto Research Lab. (United States)


Published in SPIE Proceedings Vol. 1709:
Applications of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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