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.
翻译:地面透视雷达(GPR)一直被用作树根检查的非破坏性工具。从GPR雷达图中估算根相关参数非常有利于根健康监测和成像。然而,估算根相关参数的任务具有挑战性,因为根反射是多个根参数和根方向的复杂功能。现有方法只能一次估算一个根参数,而不考虑其他参数和根方向的影响,从而在不同根条件下导致估算准确性有限。此外,土壤根参数在GPR雷达仪中引入分解,使数据处理和解释更加困难。为了解决这些问题,一个新的神经网络结构,称为掩码-导-多极整合神经网络(MMI-Net),建议同时自动估算多根相关参数,而不考虑其他参数和根方向的影响,导致在不同根条件下预测精确度。一个MasmayNet,用来预测根反射区域,以消除干扰性环境的根参数,使数据处理和解释更加困难。一个ParaNet,将预测的掩码用于将深度精确度指标纳入直径基参数的垂直方向,在多极根基数据库中发现,并且强调与根基数据库相关的数据直径数据库数据库数据库中发现的关键和直径模型。