Mitigating propagation losses in neural network pattern recognition through the atmosphere

Donald W. Hoock Jr.; John C. Giever

doi:10.1117/12.130864

16 December 1992 Mitigating propagation losses in neural network pattern recognition through the atmosphere

Donald W. Hoock Jr., John C. Giever

Proceedings Volume 1766, Neural and Stochastic Methods in Image and Signal Processing; (1992) https://doi.org/10.1117/12.130864
Event: San Diego '92, 1992, San Diego, CA, United States

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.

Citation Download Citation

Donald W. Hoock Jr. and John C. Giever "Mitigating propagation losses in neural network pattern recognition through the atmosphere", Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); https://doi.org/10.1117/12.130864

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