• DocumentCode
    777898
  • Title

    Efficient low-frequency inversion of 3-D buried objects with large contrasts

  • Author

    Cui, Tie Jun ; Qin, Yao ; Ye, Yuan ; Wu, Jing ; Wang, Gong-Li ; Chew, Weng Cho

  • Author_Institution
    Dept. of Radio Eng., Southeast Univ., Nanjing, China
  • Volume
    44
  • Issue
    1
  • fYear
    2006
  • Firstpage
    3
  • Lastpage
    9
  • Abstract
    An efficient inversion method is proposed using Cui et al.´s high-order extended Born approximations to reconstruct the conductivity object function of three-dimensional dielectric objects buried in a lossy Earth. High-order solutions of the object function are obtained, which have closed-form relations to the linear inverse-scattering solution. Because such relations can be evaluated quickly using the fast Fourier transform, the high-order solutions have similar simplicity as the linear inversion. When the contrasts of buried objects are large, the high-order solutions are much more accurate due to the approximate consideration of multiple-scattering effects within the objects. Hence, good-resolution images can be obtained for large-contrast objects using the new method by only solving a linear inverse problem. Numerical experiments have shown the validity and efficiency of the proposed method.
  • Keywords
    backscatter; buried object detection; dielectric bodies; fast Fourier transforms; geophysical signal processing; geophysical techniques; image reconstruction; inverse problems; remote sensing; 3D buried object detection; conductivity object function; dielectric object; extended Born approximation; fast Fourier transform; linear inverse scattering; low-frequency inversion; multiple scattering effect; numerical experiment; Approximation methods; Buried object detection; Computational electromagnetics; Dielectric losses; Electromagnetic scattering; Fast Fourier transforms; Image reconstruction; Inverse problems; Laboratories; Tensile stress; Born approximation; extended Born (ExBorn) approximation; high-order formulations; linear inversion; low-frequency detection; nonlinear inversion;
  • fLanguage
    English
  • Journal_Title
    Geoscience and Remote Sensing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0196-2892
  • Type

    jour

  • DOI
    10.1109/TGRS.2005.860207
  • Filename
    1564390