Next Article in Journal
Industrial Wastewater Discharge Retrieval Based on Stable Nighttime Light Imagery in China from 1992 to 2010
Next Article in Special Issue
Assessment of Complex Masonry Structures with GPR Compared to Other Non-Destructive Testing Studies
Previous Article in Journal
Estimation of Reservoir Discharges from Lake Nasser and Roseires Reservoir in the Nile Basin Using Satellite Altimetry and Imagery Data
Previous Article in Special Issue
3D Ground Penetrating Radar to Detect Tree Roots and Estimate Root Biomass in the Field
Article Menu

Export Article

Open AccessArticle

On Recovering Missing Ground Penetrating Radar Traces by Statistical Interpolation Methods

Instituto de Telecomunicaciones y Aplicaciones Multimedia, Universitat Politècnica de València, 46022 Valencia, Spain
Departamento Ingeniería de Comunicaciones, Universidad Miguel Hernández de Elche, 03202 Alicante, Spain
Author to whom correspondence should be addressed.
Remote Sens. 2014, 6(8), 7546-7565;
Received: 4 May 2014 / Revised: 7 August 2014 / Accepted: 8 August 2014 / Published: 14 August 2014
(This article belongs to the Special Issue Close-Range Remote Sensing by Ground Penetrating Radar)
PDF [2858 KB, uploaded 14 August 2014]


Missing 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
Keywords: GPR; independent component analysis; interpolation; missing data GPR; independent component analysis; interpolation; missing data

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top