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20 June 1995 Mine target detection using principal component and neural networks method
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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 for feature extraction, detection and classification of targets in six different optical bands ranging from 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.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mahmood R. Azimi-Sadjadi and Xi Miao "Mine target detection using principal component and neural networks method", Proc. SPIE 2496, Detection Technologies for Mines and Minelike Targets, (20 June 1995);

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