Reconstruction of Ground Penetrating Radar Images using techniques based on Optimization.

buried utilities;ground penetrating radar;gradient descent;deconvolution;migration;mean field annealing;optimization;image reconstruction

Suvarna, Sushil Sheena ; Dr Wesley Snyder, Committee Chair,Dr Leonhard Bernold, Committee Co-Chair,Dr Mladen Vouk, Committee Member,Dr Douglas Reeves, Committee Member,Suvarna, Sushil Sheena ; Dr Wesley Snyder ; Committee Chair ; Dr Leonhard Bernold ; Committee Co-Chair ; Dr Mladen Vouk ; Committee Member ; Dr Douglas Reeves ; Committee Member

University:North Carolina State University

关键词: buried utilities;    ground penetrating radar;    gradient descent;    deconvolution;    migration;    mean field annealing;    optimization;    image reconstruction;   

Others  :  https://repository.lib.ncsu.edu/bitstream/handle/1840.16/1644/etd.pdf?sequence=1&isAllowed=y

美国|英语

来源: null

【 摘 要 】

Ground Penetrating Radar (GPR) is an instrument used in semi-automated construction systems. In principal, images of subsurface objects such as pipes and mines may be detected and potentially measured. The detection of utilities is complicated by a combination of the complexity involved in the data collection technique of the GPR and the irregularities present beneath the surface. This thesis provides the initial results in the development of an algorithm to invert the effects of these corruptions and return images, which are exact in the placement and conformation of subterranean objects. The technique employed is a deconvolution-like method that utilizes a maximum a posteriori (MAP) based optimization method to estimate the best reconstruction. Mean field annealing (MFA) using gradient descent is the optimization method used. Using this technique, single objects in the field of observation were reconstructed to within an acceptable percentage of their original shape. Further work would involve reconstructing multiple objects in the field of observation as well as considering features other than hyperbolae that correspond to objects.