Landmine detection and classification with complex-valued hybrid neural network using scattering parameters dataset

Authors:
Chih-Chung Yang;N. K. Bose
Affiliations:
Dept. of Electr. Eng., Pennsylvania State Univ., University Park, PA, USA;-
Venue:
IEEE Transactions on Neural Networks
Year:
2005

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Abstract

Neural networks have been applied to landmine detection from data generated by different kinds of sensors. Real-valued neural networks have been used for detecting landmines from scattering parameters measured by ground penetrating radar (GPR) after disregarding phase information. This paper presents results using complex-valued neural networks, capable of phase-sensitive detection followed by classification. A two-layer hybrid neural network structure incorporating both supervised and unsupervised learning is proposed to detect and then classify the types of landmines. Tests are also reported on a benchmark data.