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29 April 2010Exploiting spatial distributions for minefield detection in cluttered environment
Spectral, shape and texture features of the detected targets are used to model the likelihood of detections to be
potential mines in a minefield. However, a large number of these potential mines can be false alarms due to the
similarity of the mine signatures with natural and other manmade clutter objects which significantly affects the
overall detection performance. In addition to the spectral features, spatial distribution of the detected targets can be
used to improve the minefield detection performance. In this paper, spectral features and spatial distributions are
used simultaneously for minefield detection. We use nearest neighbor distances of the detected targets to capture the
spatial characteristics of the minefields. We investigate the spatial distributions and evaluate minefield performance
for both patterned and scatterable minefields in a cluttered environment where the number of detected mines is many
times less than the number of false alarms. For patterned minefields, performance for minefields with different
number of rows at different mine false alarm rates is evaluated. For scatterable minefields, we evaluate the
performance of minefields where potential mines are randomly and regularly distributed. In all cases, the false
alarms are assumed to be spatially randomly distributed. The performance of the proposed detection algorithm is
compared to the baseline algorithm using extensive simulated minefield data.
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Anh Trang, Sanjeev Agarwal, Thomas Broach, Thomas Smith, "Exploiting spatial distributions for minefield detection in cluttered environment," Proc. SPIE 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV, 766426 (29 April 2010); https://doi.org/10.1117/12.851488