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Detection and classification of buried dielectric anomalies by means of the bispectrum method and neural networks

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2 Author(s)
Balan, A.N. ; Dept. of Electr. Eng., Colorado State Univ., Fort Collins, CO, USA ; Azimi-Sadjadi, M.R.

The development of a neural network-based system for detection and classification of buried landmines is the main focus of this paper. Shape-dependent features are extracted by means of the bispectrum method. These features are then applied to the neural network. A multilayer back-propagation-type neural network is trained and tested on the feature sets extracted from equally spaced radial slices of image windows. Simulation results obtained for two types of targets indicated good detection and classification rates

Published in:

Instrumentation and Measurement, IEEE Transactions on  (Volume:44 ,  Issue: 6 )

Date of Publication:

Dec 1995

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