Translator Disclaimer
18 May 2006 On the confidence level fusion of IR and forward-looking GPR
Author Affiliations +
We consider in this paper the improvement of side-attack mine detection by performing confidence level fusion with data collected from vehicle-mounted forward-looking IR and GPR (FL-GPR) sensors. The mine detection system is vehicle based, and has both IR and FL-GPR sensors mounted on the top of the vehicle. The IR images and FL-GPR data are captured as the vehicle moves forward. The detections from IR images are obtained from the Scale-Invariant Feature Transform (SIFT) and Morphological Shared-Weight Neural Networks (MSNN) depending on target characteristics, and those from FL-GPR are derived from the FL-GPR SAR images through object-tracking. Since the IR and FL-GPR alarms do not occur at the same location, the fusion process begins with each IR alarm and looks at the nearby FL-GPR alarms with confidences weighted by values that are inversely proportional to their distances to the IR alarm. The FL-GPR alarm with the highest weighted confidence is selected and combined with the IR confidence through geometric mean. An experimental dataset collected from a government test site is used for performance evaluation. At the highest Pd and comparing with IR only, fusing IR and FL-GPR yields a reduction of FAR by 26%. When the Hough transform is applied to reject the IR alarms that have irregular shapes, the fusion results provides a reduction of FAR by 35% at the highest Pd.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
T. Wang, J. M. Keller, M. Busch, P. Gader, C. Hawkins, J. McElroy, and K. C. Ho "On the confidence level fusion of IR and forward-looking GPR", Proc. SPIE 6217, Detection and Remediation Technologies for Mines and Minelike Targets XI, 62172T (18 May 2006);

Back to Top