Li Xibei, Cheng Xi, Zhao Yunjie, Xiang Binbin, Zhang Taihong
School of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China.
Xinjiang Agricultural Information Engineering Technology Research Center, Xinjiang Agricultural University, Urumqi 830052, China.
Sensors (Basel). 2025 Feb 5;25(3):947. doi: 10.3390/s25030947.
Tree roots are vital for tree ecosystems; accurate root detection helps analyze the health of trees and supports the effective management of resources such as fertilizers, water and pesticides. In this paper, a deep learning-based ground-penetrating radar (GPR) inversion method is proposed to simultaneously image the spatial distribution of permittivity for subsurface tree roots and layered heterogeneous soils in real time. Additionally, a GPR simulation data set and a measured data set are built in this study, which were used to train inversion models and validate the effectiveness of GPR inversion methods.The introduced GPR inversion model is a pyramid convolutional network with vision transformer and edge inversion auxiliary task (PyViTENet), which combines pyramidal convolution and vision transformer to improve the diversity and accuracy of data feature extraction. Furthermore, by adding the task of edge inversion of the permittivity distribution of underground materials, the model focuses more on the details of heterogeneous structures. The experimental results show that, for the case of buried scatterers in layered heterogeneous soil, the PyViTENet performs better than other deep learning methods on the simulation data set. It can more accurately invert the permittivity of scatterers and the soil stratification. The most notable advantage of PyViTENet is that it can accurately capture the heterogeneous structural details of the soil within the layer since the soil around the tree roots in the real scene is layered soil and each layer of soil is also heterogeneous due to factors such as humidity, proportion of different soil particles, etc.In order to further verify the effectiveness of the proposed inversion method, this study applied the PyViTENet to GPR measured data through transfer learning for reconstructing the permittivity, shape, and position information of scatterers in the actual scene. The proposed model shows good generalization ability and accuracy, and provides a basis for non-destructive detection of underground scatterers and their surrounding medium.
树根对树木生态系统至关重要；准确的根系检测有助于分析树木健康状况，并支持对肥料、水和农药等资源的有效管理。本文提出了一种基于深度学习的探地雷达（GPR）反演方法，用于实时成像地下树根和分层非均质土壤的介电常数空间分布。此外，本研究构建了一个GPR模拟数据集和一个实测数据集，用于训练反演模型并验证GPR反演方法的有效性。所引入的GPR反演模型是一种带有视觉Transformer和边缘反演辅助任务的金字塔卷积网络（PyViTENet），它结合了金字塔卷积和视觉Transformer，以提高数据特征提取的多样性和准确性。此外，通过添加地下材料介电常数分布的边缘反演任务，该模型更关注非均质结构的细节。实验结果表明，对于分层非均质土壤中掩埋散射体的情况，PyViTENet在模拟数据集上的表现优于其他深度学习方法。它能够更准确地反演散射体的介电常数和土壤分层情况。PyViTENet最显著的优点是，由于实际场景中树根周围的土壤是分层土壤，且每层土壤由于湿度、不同土壤颗粒比例等因素也是非均质的，所以它能够准确捕捉层内土壤的非均质结构细节。为了进一步验证所提出反演方法的有效性，本研究通过迁移学习将PyViTENet应用于GPR实测数据，以重建实际场景中散射体的介电常数、形状和位置信息。所提出的模型具有良好的泛化能力和准确性，为地下散射体及其周围介质的无损检测提供了依据。
