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Proceedings Paper

Validating spectral spatial detection based on MMPP formulation
Author(s): Anh Trang; Sanjeev Agarwal; Thomas Broach; Thomas Smith
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Paper Abstract

Spectral, shape or texture features of the detected targets are used to model the likelihood of the targets to be potential mines in a minefield. However, some potential mines can be false alarms due to the similarity of the mine signatures with natural and other manmade clutter signatures. Therefore, in addition to the target features, spatial distribution of the detected targets can be used to improve the minefield detection performance. In our recently published SPIE paper, we evaluated minefield detection performance for both patterned and unpatterned minefields in highly cluttered environments, simultaneously using both target features and target spatial distributions that define Markov Marked Point Process (MMPP). The results have suggested that proper exploitation of spectral/shape features and spatial distributions can indeed contribute improved performance of patterned and unpatterned minefield detection. Also, the ability of the algorithm to detect the minefields in highly cluttered environments shows the robustness of the developed minefield detection algorithm based on MMPP formulation. Moreover, the results show that the MMPP minefield detection algorithm performs significantly better than the baseline algorithm employing spatial point process with false alarm mitigation. Since these results were based on the simulated data, it is not clear that the MMPP detection algorithm has fully achieved its best performance. To validate its performance, an analytical solution for the minefield detection problem will be developed, and its performance will be compared with the performance of the simulated solution. The analytical solution for the complete minefield detection problem is intractable due to a large number of detections and the variation of the number of detected mines in the minefield process. Therefore, an analytical solution for a simplified detection problem will be derived, and its minefield performance will be compared with the minefield performance obtained from the simulation in the same MMPP framework for different clutter rates.

Paper Details

Date Published: 23 May 2011
PDF: 12 pages
Proc. SPIE 8017, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI, 801723 (23 May 2011); doi: 10.1117/12.886804
Show Author Affiliations
Anh Trang, U.S. Army Night Vision and Electronic Sensors Directorate (United States)
Sanjeev Agarwal, U.S. Army Night Vision and Electronic Sensors Directorate (United States)
Thomas Broach, U.S. Army Night Vision and Electronic Sensors Directorate (United States)
Thomas Smith, U.S. Army Night Vision and Electronic Sensors Directorate (United States)


Published in SPIE Proceedings Vol. 8017:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI
Russell S. Harmon; John H. Holloway; J. Thomas Broach, Editor(s)

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