Holographic neural networks versus conventional neural networks: a comparative evaluation for the classification of landmine targets in ground-penetrating radar images

Naga R. Mudigonda; Ray Kacelenga; Mark Edwards

doi:10.1117/12.532532

21 September 2004 Holographic neural networks versus conventional neural networks: a comparative evaluation for the classification of landmine targets in ground-penetrating radar images

Naga R. Mudigonda, Ray Kacelenga, Mark Edwards

Author Affiliations +

Proceedings Volume 5415, Detection and Remediation Technologies for Mines and Minelike Targets IX; (2004) https://doi.org/10.1117/12.532532
Event: Defense and Security, 2004, Orlando, Florida, United States

Abstract

This paper evaluates the performance of a holographic neural network in comparison with a conventional feedforward backpropagation neural network for the classification of landmine targets in ground penetrating radar images. The data used in the study was acquired from four different test sites using the landmine detection system developed by General Dynamics Canada Ltd., in collaboration with the Defense Research and Development Canada, Suffield. A set of seven features extracted for each detected alarm is used as stimulus inputs for the networks. The recall responses of the networks are then evaluated against the ground truth to declare true or false detections. The area computed under the receiver operating characteristic curve is used for comparative purposes. With a large dataset comprising of data from multiple sites, both the holographic and conventional networks showed comparable trends in recall accuracies with area values of 0.88 and 0.87, respectively. By using independent validation datasets, the holographic network’s generalization performance was observed to be better (mean area = 0.86) as compared to the conventional network (mean area = 0.82). Despite the widely publicized theoretical advantages of the holographic technology, use of more than the required number of cortical memory elements resulted in an over-fitting phenomenon of the holographic network.

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Naga R. Mudigonda, Ray Kacelenga, and Mark Edwards "Holographic neural networks versus conventional neural networks: a comparative evaluation for the classification of landmine targets in ground-penetrating radar images", Proc. SPIE 5415, Detection and Remediation Technologies for Mines and Minelike Targets IX, (21 September 2004); https://doi.org/10.1117/12.532532

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KEYWORDS

Land mines

System on a chip

Sensors

Neural networks

General packet radio service

Holography

Image classification

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