Independent component analysis for GPR based hand held mine detection

Partha Pratim Palit; Sanjeev Agarwal

doi:10.1117/12.479109

13 August 2002 Independent component analysis for GPR-based handheld mine detection

Partha Pratim Palit, Sanjeev Agarwal

Proceedings Volume 4742, Detection and Remediation Technologies for Mines and Minelike Targets VII; (2002) https://doi.org/10.1117/12.479109
Event: AeroSense 2002, 2002, Orlando, FL, United States

Abstract

Unlike Vehicle-mounted ground penetrating radar (GPR), the hand-held GPR data is highly variable. In this paper we propose an independent component analysis (ICA) based approach for processing hand held stepped frequency GPR data for mine detection. ICA is a linear transformation, which seeks prominent features in high-dimensional data. Compared to principal component analysis (PCA), which searches for basis vectors in the direction of maximum variance, ICA finds more interesting features in the direction of maximum non-gaussianity. In our current implementation, ICA is used to find a set of basis vectors corresponding to the background clutter. Residual error for this GPR with respect to ICA clutter basis shows the presence or absence of landmine. The performance of the ICA based detection is compared with the correlation detector for GPR only data for hand held mine detection. Comparative receiver operating characteristics (ROC) curves representing probability of detection verses false alarm rate are shown for both scan and investigative mode for ICA based detection and correlation detection.

Citation Download Citation

Partha Pratim Palit and Sanjeev Agarwal "Independent component analysis for GPR-based handheld mine detection", Proc. SPIE 4742, Detection and Remediation Technologies for Mines and Minelike Targets VII, (13 August 2002); https://doi.org/10.1117/12.479109

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