Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162648
Title: Estimating parameters of the tree root in heterogeneous soil environments via mask-guided multi-polarimetric integration neural network
Authors: Sun, Hai-Han
Lee, Yee Hui
Dai, Qiqi
Li, Chongyi
Ow, Genevieve
Mohamed Lokman Mohd Yusof
Yucel, Abdulkadir C.
Keywords: Engineering::Computer science and engineering
Engineering::Electrical and electronic engineering
Issue Date: 2021
Source: Sun, H., Lee, Y. H., Dai, Q., Li, C., Ow, G., Mohamed Lokman Mohd Yusof & Yucel, A. C. (2021). Estimating parameters of the tree root in heterogeneous soil environments via mask-guided multi-polarimetric integration neural network. IEEE Transactions On Geoscience and Remote Sensing, 60, 1-16. https://dx.doi.org/10.1109/TGRS.2021.3138974
Journal: IEEE Transactions on Geoscience and Remote Sensing
Abstract: 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.
URI: https://hdl.handle.net/10356/162648
ISSN: 0196-2892
DOI: 10.1109/TGRS.2021.3138974
Rights: © 2021 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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