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

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2 Author(s)
Chih-Chung Yang ; Dept. of Electr. Eng., Pennsylvania State Univ., University Park, PA, USA ; N. K. Bose

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.

Published in:

IEEE Transactions on Neural Networks  (Volume:16 ,  Issue: 3 )