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454 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 9, NO. 3, MAY 1998 Detection of Mines and Minelike Targets Using
 

Summary: 454 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 9, NO. 3, MAY 1998
Detection of Mines and Minelike Targets Using
Principal Component and Neural-Network Methods
Xi Miao, Mahmood R. Azimi-Sadjadi, Senior Member, IEEE, Bin Tian,
Abinash C. Dubey, Associate Member, IEEE, and Ned H. Witherspoon
Abstract-- 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 sys-
tem 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

  

Source: Azimi-Sadjadi, Mahmood R. - Department of Electrical and Computer Engineering, Colorado State University

 

Collections: Computer Technologies and Information Sciences