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

Author

Balan, Ajay N. ; Azimi-Sadjadi, Mohamood R.

Author_Institution

Dept. of Electr. Eng., Colorado State Univ., Fort Collins, CO, USA

Volume

44

Issue

6

fYear

1995

fDate

12/1/1995 12:00:00 AM

Firstpage

998

Lastpage

1002

Abstract

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

Keywords

feature extraction; feedforward neural nets; military systems; multilayer perceptrons; bispectrum method; buried dielectric anomalies; buried object classification; buried object detection; equally spaced radial slices; feature extraction; image windows; landmines; multilayer back-propagation-type neural network; neural networks; shape-dependent features; target detection; Dielectrics; Feature extraction; Focusing; Fourier transforms; Karhunen-Loeve transforms; Landmine detection; Multi-layer neural network; Neural networks; Object detection; Shape measurement;

fLanguage

English

Journal_Title

Instrumentation and Measurement, IEEE Transactions on

Publisher

ieee

ISSN

0018-9456

Type

jour

DOI

10.1109/19.475145

Filename

475145