Title :
Three-dimensional imaging of buried objects in very lossy earth by inversion of VETEM data
Author :
Cui, Tie Jun ; Aydiner, Alaeddin A. ; Chew, Weng Cho ; Wright, D.L. ; Smith, D.V.
Author_Institution :
Dept. of Radio Eng., Southeast Univ., Nanjing, China
Abstract :
The very early time electromagnetic system (VETEM) is an efficient tool for the detection of buried objects in very lossy earth, which allows a deeper penetration depth compared to the ground-penetrating radar. In this paper, the inversion of VETEM data is investigated using three-dimensional (3-D) inverse scattering techniques, where multiple frequencies are applied in the frequency range from 0-5 MHz. For small and moderately sized problems, the Born approximation and/or the Born iterative method have been used with the aid of the singular value decomposition and/or the conjugate gradient method in solving the linearized integral equations. For large-scale problems, a localized 3-D inversion method based on the Born approximation has been proposed for the inversion of VETEM data over a large measurement domain. Ways to process and to calibrate the experimental VETEM data are discussed to capture the real physics of buried objects. Reconstruction examples using synthesized VETEM data and real-world VETEM data are given to test the validity and efficiency of the proposed approach.
Keywords :
buried object detection; conjugate gradient methods; dielectric bodies; electromagnetic wave scattering; geophysical techniques; image reconstruction; inverse problems; radiowave propagation; singular value decomposition; 0 to 5 MHz; Born approximation; Born iterative method; VETEM data; buried objects; conjugate gradient method; inverse scattering techniques; linearized integral equations; localized 3-D inversion method; penetration depth; reconstruction; singular value decomposition; three-dimensional imaging; very early time electromagnetic system; very lossy earth; Approximation methods; Buried object detection; Earth; Frequency; Ground penetrating radar; Inverse problems; Iterative methods; Radar detection; Scattering; Singular value decomposition;
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
DOI :
10.1109/TGRS.2003.815974