On Recovering Missing Ground Penetrating Radar Traces by Statistical Interpolation Methods
AbstractMissing traces in ground penetrating radar (GPR) B-scans (radargrams) may appear because of limited scanning resolution, failures during the acquisition process or the lack of accessibility to some areas under test. Four statistical interpolation methods for recovering these missing traces are compared in this paper: Kriging, Wiener structures, Splines and the expectation assuming an independent component analyzers mixture model (E-ICAMM). Kriging is an adaptation to the spatial context of the linear least mean squared error estimator. Wiener structures improve the linear estimator by including a nonlinear scalar function. Splines are a commonly used method to interpolate GPR traces. This consists of piecewise-defined polynomial curves that are smooth at the connections (or knots) between pieces. E-ICAMM is a new method proposed in this paper. E-ICAMM consists of computing the optimum nonlinear estimator (the conditional mean) assuming a non-Gaussian mixture model for the joint probability density in the observation space. The proposed methods were tested on a set of simulated data and a set of real data, and four performance indicators were computed. Real data were obtained by GPR inspection of two replicas of historical walls. Results show the superiority of E-ICAMM in comparison with the other three methods in the application of reconstructing incomplete B-scans. View Full-Text
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Safont, G.; Salazar, A.; Rodriguez, A.; Vergara, L. On Recovering Missing Ground Penetrating Radar Traces by Statistical Interpolation Methods. Remote Sens. 2014, 6, 7546-7565.
Safont G, Salazar A, Rodriguez A, Vergara L. On Recovering Missing Ground Penetrating Radar Traces by Statistical Interpolation Methods. Remote Sensing. 2014; 6(8):7546-7565.Chicago/Turabian Style
Safont, Gonzalo; Salazar, Addisson; Rodriguez, Alberto; Vergara, Luis. 2014. "On Recovering Missing Ground Penetrating Radar Traces by Statistical Interpolation Methods." Remote Sens. 6, no. 8: 7546-7565.