A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Automated Detection of Reflection Hyperbolas in Complex GPR Images With No A Priori Knowledge on the Medium
In this paper, we propose an automated detection algorithm for well- and ill-shaped ground-penetrating radar reflection hyperbolas for complex media, calibrated with human recognition principles. The algorithm detects the apex of the hyperbolas by fitting an analytical function of a hyperbola to the profile edge dots detected with a Canny filter. The existence of a hyperbola is determined using a set of carefully chosen criteria calibrated in order to fit the ambiguity zone for the human brain. The inherent misshapedness of field hyperbolas is further considered by defining a buffer zone around the theoretical hyperbola. First, the method was tested in the laboratory over tree roots and PVC pipes and on field images over tree root systems. Both time- and frequency-domain radars were used on-ground. After around 1-3 min of computation time for 10 000 edge dots in a MATLAB environment (single 1.96-GHz processor), the results showed rates of false alarm and nondetection of maximum 20% and 28%, respectively. In comparison with the semiautomated hyperbola detection provided by a commercial software, these rates were lower. Second, we conducted a sensitivity analysis to estimate the validity of the fitting of a hyperbola equation neglecting the object radius. The fitting was close, but the derivation of the relative permittivity from the analytical equation neglecting the radius led to high errors. In conclusion, owing to the low computational time and its good performances, the proposed algorithm is suitable for complex environments.
Automated Detection of Reflection Hyperbolas in Complex GPR Images With No A Priori Knowledge on the Medium
In this paper, we propose an automated detection algorithm for well- and ill-shaped ground-penetrating radar reflection hyperbolas for complex media, calibrated with human recognition principles. The algorithm detects the apex of the hyperbolas by fitting an analytical function of a hyperbola to the profile edge dots detected with a Canny filter. The existence of a hyperbola is determined using a set of carefully chosen criteria calibrated in order to fit the ambiguity zone for the human brain. The inherent misshapedness of field hyperbolas is further considered by defining a buffer zone around the theoretical hyperbola. First, the method was tested in the laboratory over tree roots and PVC pipes and on field images over tree root systems. Both time- and frequency-domain radars were used on-ground. After around 1-3 min of computation time for 10 000 edge dots in a MATLAB environment (single 1.96-GHz processor), the results showed rates of false alarm and nondetection of maximum 20% and 28%, respectively. In comparison with the semiautomated hyperbola detection provided by a commercial software, these rates were lower. Second, we conducted a sensitivity analysis to estimate the validity of the fitting of a hyperbola equation neglecting the object radius. The fitting was close, but the derivation of the relative permittivity from the analytical equation neglecting the radius led to high errors. In conclusion, owing to the low computational time and its good performances, the proposed algorithm is suitable for complex environments.
Automated Detection of Reflection Hyperbolas in Complex GPR Images With No A Priori Knowledge on the Medium
Laurence Mertens (author) / Sebastien Lambot / Raffaele Persico / Loredana Matera
2016
Article (Journal)
English
Local classification TIB:
770/3710/5670
BKL:
38.03
Methoden und Techniken der Geowissenschaften
/
74.41
Luftaufnahmen, Photogrammetrie
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