  Ground-penetrating radar (GPR) has been used as a non-destructive tool for
tree root inspection. Estimating root-related parameters from GPR radargrams
greatly facilitates root health monitoring and imaging. However, the task of
estimating root-related parameters is challenging as the root reflection is a
complex function of multiple root parameters and root orientations. Existing
methods can only estimate a single root parameter at a time without considering
the influence of other parameters and root orientations, resulting in limited
estimation accuracy under different root conditions. In addition, soil
heterogeneity introduces clutter in GPR radargrams, making the data processing
and interpretation even harder. To address these issues, a novel neural network
architecture, called mask-guided multi-polarimetric integration neural network
(MMI-Net), is proposed to automatically and simultaneously estimate multiple
root-related parameters in heterogeneous soil environments. The MMI-Net
includes two sub-networks: a MaskNet that predicts a mask to highlight the root
reflection area to eliminate interfering environmental clutter, and a ParaNet
that uses the predicted mask as guidance to integrate, extract, and emphasize
informative features in multi-polarimetric radargrams for accurate estimation
of five key root-related parameters. The parameters include the root depth,
diameter, relative permittivity, horizontal and vertical orientation angles.
Experimental results demonstrate that the proposed MMI-Net achieves high
estimation accuracy in these root-related parameters. This is the first work
that takes the combined contributions of root parameters and spatial
orientations into account and simultaneously estimates multiple root-related
parameters. The data and code implemented in the paper can be found at
https://haihan-sun.github.io/GPR.html.
