Detection of mines and minelike targets using principal component and neural-network methods Miao, Xi ; Azimi-Sadjadi, Mahmood R. ; Tian, Bin ; Dubey, Abinash C. ; Witherspoon, Ned H. This paper introduces a new system for real-time detection and classification of arbitrarily scattered surface-laid mines from multispectral imagery data of a minefield. The system consists of six channels which use various neural-network structures for feature extraction, detection, and classification of targets in six different optical bands ranging from near UV to near IR. A single-layer autoassociative network trained using the recursive least square (RLS) learning rule was employed in each channel to perform feature extraction. Based upon the extracted features, two different neural-network architectures were used and their performance was compared against the standard maximum likelihood (ML) classification scheme. The outputs of the detector/classifier network in all the channels were fused together in a final decision-making system. Two different final decision making schemes using the majority voting and weighted combination based on consensual theory were considered. Simulations were performed on real data for six bands and on several images in order to account for the variations in size, shape, and contrast of the targets and also the signal-to-clutter ratio. The overall results showed the promise of the proposed system for detection and classification of mines and minelike tagets. Colorado State University. Libraries 1998 text ; image application/pdf ECEmra00052.pdf FACFECEN100532ARTI eng c1998 IEEE
Detection of mines and minelike targets using principal component and neural-network methods
Miao, Xi ; Azimi-Sadjadi, Mahmood R. ; Tian, Bin ; Dubey, Abinash C. ; Witherspoon, Ned H.
This paper introduces a new system for real-time detection and classification of arbitrarily scattered surface-laid mines from multispectral imagery data of a minefield. The system consists of six channels which use various neural-network structures for feature extraction, detection, and classification of targets in six different optical bands ranging from near UV to near IR. A single-layer autoassociative network trained using the recursive least square (RLS) learning rule was employed in each channel to perform feature extraction. Based upon the extracted features, two different neural-network architectures were used and their performance was compared against the standard maximum likelihood (ML) classification scheme. The outputs of the detector/classifier network in all the channels were fused together in a final decision-making system. Two different final decision making schemes using the majority voting and weighted combination based on consensual theory were considered. Simulations were performed on real data for six bands and on several images in order to account for the variations in size, shape, and contrast of the targets and also the signal-to-clutter ratio. The overall results showed the promise of the proposed system for detection and classification of mines and minelike tagets.
Colorado State University. Libraries
1998
text ; image
application/pdf
ECEmra00052.pdf
FACFECEN100532ARTI
eng
c1998 IEEE