A typical minefield detection approach is based on a sequential processing employing mine detection and false alarm rejection followed by minefield detection. The current approach does not work robustly under different backgrounds and environment conditions because target signature changes with time and its performance degrades in the presence of high density of false alarms. The aim of this research will be to advance the state of the art in detection of both patterned and unpatterned minefield in high clutter environments. The proposed method seeks to combine false alarm rejection module and the minefield detection module of the current architecture by spatial-spectral clustering and inference module using a Markov Marked Point Process formulation. The approach simultaneously exploits the feature characteristics of the target signature and spatial distribution of the targets in the interrogation region. The method is based on the premise that most minefields can be characterized by some type of distinctive spatial distribution of "similar" looking mine targets. The minefield detection problem is formulated as a Markov Marked Point Process (MMPP) where the set of possible mine targets is divided into a possibly overlapping mixture of targets. The likelihood of the minefield depends simultaneously on feature characteristics of the target and their spatial distribution. A framework using "Belief Propagation" is developed to solve the minefield inference problem based on MMPP. Preliminary investigation using simulated data shows the efficacy of the approach.

Date Published: 7 May 2007
PDF: 12 pages
Proc. SPIE 6553, Detection and Remediation Technologies for Mines and Minelike Targets XII, 655313 (7 May 2007); doi: 10.1117/12.721368

Published in SPIE Proceedings Vol. 6553:
Detection and Remediation Technologies for Mines and Minelike Targets XII
Russell S. Harmon; J. Thomas Broach; John H. Holloway Jr., Editor(s)