Detection and classification of buried dielectric anomalies using a separated aperture sensor and a neural network discriminator Azimi-Sadjadi, Mahmood R. ; Poole, David E. ; Sheedvash, Sassan ; Sherbondy, Kelly D. ; Stricker, Scott A. The problem of detection and classification of buried dielectric anomalies using a separated aperture microwave sensor and an artificial neural network discriminator was considered. Several methods for training and data representation were developed to study the trainability and generalization capabilities of the networks. The effect of the architectural variation on the network performance was also studied. The principal component method was used to reduce the volume of the data and also the dimension of the weight space. Simulation results on two types of targets were obtained which indicated superior detection and classification performance when compared with the conventional methods. Colorado State University. Libraries 1992 text ; image application/pdf ECEmra00035.pdf FACFECEN100515ARTI eng c1992 IEEE
Detection and classification of buried dielectric anomalies using a separated aperture sensor and a neural network discriminator
Azimi-Sadjadi, Mahmood R. ; Poole, David E. ; Sheedvash, Sassan ; Sherbondy, Kelly D. ; Stricker, Scott A.
The problem of detection and classification of buried dielectric anomalies using a separated aperture microwave sensor and an artificial neural network discriminator was considered. Several methods for training and data representation were developed to study the trainability and generalization capabilities of the networks. The effect of the architectural variation on the network performance was also studied. The principal component method was used to reduce the volume of the data and also the dimension of the weight space. Simulation results on two types of targets were obtained which indicated superior detection and classification performance when compared with the conventional methods.
Colorado State University. Libraries
1992
text ; image
application/pdf
ECEmra00035.pdf
FACFECEN100515ARTI
eng
c1992 IEEE