Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162648
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dc.contributor.authorSun, Hai-Hanen_US
dc.contributor.authorLee, Yee Huien_US
dc.contributor.authorDai, Qiqien_US
dc.contributor.authorLi, Chongyien_US
dc.contributor.authorOw, Genevieveen_US
dc.contributor.authorMohamed Lokman Mohd Yusofen_US
dc.contributor.authorYucel, Abdulkadir C.en_US
dc.date.accessioned2022-11-02T01:55:09Z-
dc.date.available2022-11-02T01:55:09Z-
dc.date.issued2021-
dc.identifier.citationSun, 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.2021.3138974en_US
dc.identifier.issn0196-2892en_US
dc.identifier.urihttps://hdl.handle.net/10356/162648-
dc.description.abstractGround-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.en_US
dc.description.sponsorshipMinistry of National Development (MND)en_US
dc.description.sponsorshipNational Parks Boarden_US
dc.language.isoenen_US
dc.relation.ispartofIEEE Transactions on Geoscience and Remote Sensingen_US
dc.rights© 2021 IEEE. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleEstimating parameters of the tree root in heterogeneous soil environments via mask-guided multi-polarimetric integration neural networken_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doi10.1109/TGRS.2021.3138974-
dc.identifier.scopus2-s2.0-85122332082-
dc.identifier.volume60en_US
dc.identifier.spage1en_US
dc.identifier.epage16en_US
dc.subject.keywordsDeep Learningen_US
dc.subject.keywordsGround-Penetrating Radaren_US
dc.description.acknowledgementThis work was supported by the Ministry of National Development Research Fund, National Parks Board, Singapore.en_US
item.fulltextNo Fulltext-
item.grantfulltextnone-
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