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 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 signalto-clutter ratio. The overall results showed the promise of the proposed system for detection and classification of mines and minelike tagets.

This paper proposes a new system for real-time detection and classification of arbitrarily scattered surface-laid mines. The system consists of six channels which use various neural network structures f or feature extraction, detection and classification of targets in six different optical bands ranging f rom near UV to near IR. A single-layer auto-associative neural network trained by the Recursive Least Square (RLS) learning rule was employed in each channel to perform target feature extraction. The detection/classification based upon the extracted features was accomplished by a three-layer back-propagation neural network with 11-25-10-1 architecture. The outputs of the detector/classifier network in all the channels are fused together in a final decision making system. Simulations were performed on real data for six bands. Forty-eight different images were used 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 for the combined system showed a detection rate of approximately 97%, with less than 3% false alarm rate.