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18 October 2001 Surface mine detection in the 3- to 5-μm band using neural networks
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Abstract
This paper discusses some preliminary results of the application of simple neural networks to the problem of landmine detection in IR imagery. A large data set of IR imagery (3-5)mum) collected as part of the U.S. Army's Lightweight Airborne Multispectral Minefield Detection (LAMD) system is used as the basis for the analysis. The data set is divided into training and testing subsets then used to train and evaluate the performance of some neural networks. A single neuron perceptron is trained and evaluated using two different types of input feature. The first type of input feature is based on the raw pixel values with typical maximum vale normalization. The second type is based on the unity vector of the inputs to take advantage of the angular displacement feature of the vector [1]. A more complex, multiple neuron network is also trained and evaluated. The results are compared to determine whether the increased computational complexity of the multiple neuron network is justified in terms of improved performance.
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Thomas P. Donzelli and Hanna Tran Haskett "Surface mine detection in the 3- to 5-μm band using neural networks", Proc. SPIE 4394, Detection and Remediation Technologies for Mines and Minelike Targets VI, (18 October 2001); https://doi.org/10.1117/12.445483
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