Detection of mines and minelike targets using principal component and neural-network methods

— 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...

• Type: Journal
• Year: 1998
• Where: TNN
• Authors: Xi Miao, Mahmood R. Azimi-Sadjadi, Bin Tan, A. C. Dubey, N. H. Witherspoon
• Keywords: Feature Extraction, Multispectral Imagery Data, TNN 1998, Various Neural-network Structures