Tree roots reconstruction framework for accurate positioning in heterogeneous soil

Abstract

Ground-penetrating radar has recently found wide application in the underground imaging of tree roots. However, ignoring the random and complex nature of the heterogeneous soil and assuming the soil's relative permittivity constant throughout the survey region may yield an inaccurate tree root positioning. Meanwhile, the incompatible relative soil permittivity results in low image quality of the roots reconstruction. Furthermore, the soil's spatial heterogeneity introduces unwanted environmental clutter in the mapping of the tree root. A data processing framework is proposed to address these issues for retrieving the tree roots in heterogeneous soil environments. The proposed framework combines four techniques to be applied consecutively: First, a hyperbola extraction method based on a column-connection clustering algorithm is used to extract individual hyperbolae in B-scans, eliminate mutual influence in the process, and suppress noise. Second, an improved Hough transform technique is adopted to estimate the equivalent permittivity of each root's surrounding soil environment for each extracted hyperbola. Third, individual root restoration is done by transferring each hyperbola to a spot using its corresponding soil equivalent permittivity. Finally, individually restored features are combined in the final image. The images obtained via the proposed framework show a well reconstructed two-dimensional tree roots scenario. The applicability and the effectiveness of the proposed framework have been demonstrated through numerical simulations and field measurements.