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Proceedings Paper

Mitigating propagation losses in neural network pattern recognition through the atmosphere
Author(s): Donald W. Hoock Jr.; John C. Giever
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Paper Abstract

Neural network, image-based pattern recognition is generally robust to noise. However, when applied to imaging through the atmosphere, image-based classification performance can be severely reduced by low contrast atmospheric conditions. In particular, we show that classification performance through spatially fluctuating plumes of smoke and dust is reduced by the changes in path radiance and transmittance across the image. However, by predicting the quantitative effects of propagation losses, we also show that classification performance can be significantly improved by applying a novel training strategy. Improved performance can be obtained by training a neural network on atmospheric propagation effects as an additional class and simultaneously training the network to ignore the atmospheric influence on the target classes. Successful tests of the method in actual field measurements of targets partially obscured by smoke and dust are shown. Effects both on single layer and multi-layer backpropagation neural networks are considered, and performance improvement is shown for several classification examples.

Paper Details

Date Published: 16 December 1992
PDF: 13 pages
Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); doi: 10.1117/12.130864
Show Author Affiliations
Donald W. Hoock Jr., U.S. Army Atmospheric Sciences Lab. (United States)
John C. Giever, New Mexico State Univ. (United States)

Published in SPIE Proceedings Vol. 1766:
Neural and Stochastic Methods in Image and Signal Processing
Su-Shing Chen, Editor(s)

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