Multisensor data fusion for surface land-mine detection |
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Authors: | Filippidis A. Jain L.C. Martin N. |
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Affiliation: | Div. of Land Oper., Defence Sci. & Technol. Organ., Salisbury, SA; |
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Abstract: | Receiver operating characteristic (ROC) curves have been used to examine a novel target recognition system using a number of knowledge-based techniques to automatically detect surface land mines that are present in 30 sets of thermal and multispectral images. A summary of the results, graphed at a probability of detection greater than or equal to 96%, shows the false-alarm rates (FARs) obtained using various combinations of fusing sensors and neural classifiers averaged over the 30 images. The results show that using two neural-network classifiers on the input textural and spectral characteristics of selected multispectral bands, we obtained FARs of approximately 3%. Using polarization-resolved images only, we obtained FARs of 1.15%. Fusing the best classifier output with the polarization-resolved images, we obtained FARs as low as 0.023%. This result has shown the large improvement gained in the sensor fusion. Also, an improvement is shown by comparing these results with those reported in an existing commercial system |
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