Feed-forward neural networks to solve the closely-spaced objects problem

Thomas J. Bartolac; Ed P. Andert Jr.

doi:10.1117/12.179087

6 July 1994 Feed-forward neural networks to solve the closely-spaced objects problem

Thomas J. Bartolac, Ed P. Andert Jr.

Proceedings Volume 2235, Signal and Data Processing of Small Targets 1994; (1994) https://doi.org/10.1117/12.179087
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States

Abstract

We have applied a feed-forward neural network to the task of resolving closely-spaced objects (CSO). Traditional algorithmic methods are computationally expensive or numerically unstable, and techniques based on ad hoc rules are too subjective. Our approach relies on the principle that a sufficiently complex neural network can approximate an arbitrary function to an arbitrary degree of accuracy. We train a neural network to approximate the multi- dimensional function that maps from detector signal space to CSO parameter space, using an aggressive Hessian-based training algorithm and training set examples synthesized from the known inverse function. We find two important empirical results: we can simultaneously identify when the training set size is sufficient to adequately represent the mapping function, and when the network has achieved optimum generalization capability, for a given degree of network complexity. Thus we can predict the network and training set sizes necessary to achieve a given mission performance. Finally, we show how such a network can be used to provide sub-pixel resolution capabilities for missions observing both single objects and CSOs, as part of a real-time 2D sensor processor.

Citation Download Citation

Thomas J. Bartolac and Ed P. Andert Jr. "Feed-forward neural networks to solve the closely-spaced objects problem", Proc. SPIE 2235, Signal and Data Processing of Small Targets 1994, (6 July 1994); https://doi.org/10.1117/12.179087

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