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Ground-penetrating radar (GPR) has been used as a non-destructive tool fortree root inspection. Estimating root-related parameters from GPR radargramsgreatly facilitates root health monitoring and imaging. However, the task ofestimating root-related parameters is challenging as the root reflection is acomplex function of multiple root parameters and root orientations. Existingmethods can only estimate a single root parameter at a time without consideringthe influence of other parameters and root orientations, resulting in limitedestimation accuracy under different root conditions. In addition, soilheterogeneity introduces clutter in GPR radargrams, making the data processingand interpretation even harder. To address these issues, a novel neural networkarchitecture, called mask-guided multi-polarimetric integration neural network(MMI-Net), is proposed to automatically and simultaneously estimate multipleroot-related parameters in heterogeneous soil environments. The MMI-Netincludes two sub-networks: a MaskNet that predicts a mask to highlight the rootreflection area to eliminate interfering environmental clutter, and a ParaNetthat uses the predicted mask as guidance to integrate, extract, and emphasizeinformative features in multi-polarimetric radargrams for accurate estimationof 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 highestimation accuracy in these root-related parameters. This is the first workthat takes the combined contributions of root parameters and spatialorientations into account and simultaneously estimates multiple root-relatedparameters. The data and code implemented in the paper can be found athttps://haihan-sun.github.io/GPR.html.

Estimating Parameters of the Tree Root in Heterogeneous Soil Environments via Mask-Guided Multi-Polarimetric Integration Neural Network