基于深度神经网络的探地雷达杂波抑制和根参数预测方法

Suppressing ground penetrating radar clutter to predict root parameters using deep neural networks

为解决探地雷达(ground penetrating radar,GPR)异质土壤环境下树木根系检测图像存在背景杂波,影响检测精度,并且其数据的解译自动化程度低、成本高的问题,该研究提出一种基于深度神经网络(deep neural networks,DNN)的探地雷达杂波抑制和根参数预测方法。首先引入注意力机制优化U-net模型构成杂波抑制网络,更好地关注目标根系的双曲线反射,去除土壤异质性和雷达天线之间耦合带来的杂波影响,然后将杂波抑制前后的两张图像并行输入根参数估计网络,利用inception的多尺度感受野,挖掘全局特征和局部特征,同时预测根深度和根半径。利用仿真数据和合成真实数据构成的数据集验证方法的可行性,并完成了实地埋根试验。基于数据集的试验结果表明,该方法对于根半径预测的平均绝对误差为1.7 mm,R^(2)值为0.914,根深度预测的平均绝对误差为6.3 mm,R^(2)值为0.989;埋根试验的结果证明该方法对于根半径预测的最大误差为1.85 mm,根深度预测的最大误差为13.6 mm,平均相对误差为6.55%,实现了对根半径和根深度的准确预测。研究结果有助于为果树健康管理以及为古树名木保护提供决策参考。

Non-destructive testing of tree roots has the vast application potential to the root system evaluation of fruit and ancient trees for the better management of plant health.Among them,ground-penetrating radar(GPR)can be expected to nondestructively detect the tree roots,due to the portable and low-cost.However,the interpretation of GPR data is dependent mainly on the manual analysis,leading to the reduced automation and accuracy.Moreover,the clutters and noise that caused by soil heterogeneity can severely affect the accuracy of root detection and identification.In this study,a detection method was proposed for the clutter suppression and root parameter prediction of ground penetrating radar.The data collection was divided into two parts:the dataset and field test.The datasets consisted of simulated and synthesized real data.The roots with the radius ranging from 5 to 30 mm were randomly distributed within 300 mm underground.The field experiment was conducted in three sand pits.The soil heterogeneity was simulated to create the different moisture levels,when adding water into different parts in two of the dry sand pits.The rest sandpit was a condition of higher water content after heavy rainfall.At the same time,the roots were excavated to verify the model under real soil conditions.Four test pits contained the roots of different tree species,the different soil environments,as well as the different radii and depths.The attention mechanism was integrated into the U-net model,in order to enhance the capability in the clutter suppression and root target reflection restoration.A clutter suppression network was constructed to test the effectiveness of the model.The encoding-decoding structure of U-net was separated the target root hyperbolic reflection from the overall background.The attention mechanism enabled the network to better adaptively focus on the hyperbolic reflection of the target root system without identifying the clutter as the target reflection.The clutter suppression was realized for the original B-scan image to remove the adverse effects,due to the soil heterogeneity and radar antenna coupling.Furthermore,the network of root parameter prediction was constructed using residual blocks and inception.The multi-scale receptive field of inception was used to extract the global and local features,and simultaneously to predict the root radius and depth.The predicted results were obtained by parallel inputting the clutter suppressed image and the original B-scan image into the network.The effectiveness of the model was evaluated using datasets and field experiments.The test results of clutter suppression were evaluated using peak signal to noise ratio(PSNR)and structural similarity(SSIM),which were 39.42 dB and 0.991,respectively,better than before.The test results on datasets showed the mean absolute error(MAE)of 1.7 mm,and the coefficient of determination R^(2)value of 0.914 for the root radius prediction,while the mean absolute error(MAE)of 6.3 mm and the R^(2)value of 0.989 for the root depth prediction.The clutter suppression was also achieved in the better performance than the rest on the field data.The maximum prediction errors for the root radius and depth were 1.85,and 13.6 mm,respectively,where the total average relative error was 6.55%.Influencing factors were determined for the prediction effectiveness of ground-penetrating radar.The future research directions were pointed out as well.The findings can be applied to the actual prediction of root depth and radius of tree roots,particularly for the health administration of fruit trees and the protection of ancient trees.protection of ancient trees.protection of ancient trees.