Exploiting spatial distributions for minefield detection in cluttered environment

摘要

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.