Slice-Relation-Clustering Framework via Horizontal Angle Information for 3-D Tree Roots Reconstruction

被引:1
作者
Luo, Wenhao [1 ]
Lee, Yee Hui [1 ]
Ow, Lai Fern [2 ]
Yusof, Mohamed Lokman Mohd [2 ]
Yucel, Abdulkadir C. [1 ]
机构
[1] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore
[2] Natl Pk Board, Singapore 259569, Singapore
来源
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2023年 / 61卷
关键词
Three-dimensional displays; Estimation; Scattering parameters; Reflection; Image reconstruction; Graphical models; Distribution functions; 3-D reconstruction; ground penetrating radar (GPR); horizontal angle estimation; roots' spatial distribution; slice-relation-clustering (SRC); tree root system; GROUND-PENETRATING RADAR; SOIL;
D O I
10.1109/TGRS.2023.3272743
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
Tree root system 3-D reconstruction and spatial distribution analysis are the prevalent aspects of tree root investigation using ground penetrating radar (GPR). Precedent 3-D reconstruction methods are found to be effective in mapping simple, smooth root structures. However, repetitive and dense B-scans are needed; otherwise, the retrieved roots' spatial distribution and growth extension trend accuracy would deteriorate with the increase in the root systems' complexity. To address these issues, this article, for the first time, explores the possibility of integrating the horizontal angle information of the tree roots and a slice-relation-clustering (SRC) algorithm to reconstruct the complex tree root systems in a 3-D manner. The proposed framework, which takes the roots' horizontal angle as an analyzing condition instead of biological properties that are similar among neighboring branches used in the existing methods, clusters preprocessed and focused 2-D reflection patterns from the same single root together. The whole roots system is the combination of every single root cluster. Real measurement results show that our proposed method achieves a high efficiency in accurate root system reconstruction.
引用
收藏
页数:10
相关论文
共 31 条
  • [1] A Processing Framework for Tree-Root Reconstruction Using Ground-Penetrating Radar Under Heterogeneous Soil Conditions
    Aboudourib, Abderrahmane
    Serhir, Mohammed
    Lesselier, Dominique
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2021, 59 (01): : 208 - 219
  • [2] Unmanned Aerial Vehicle-Based Ground-Penetrating Radar Systems A review
    Alvarez Lopez, Yuri
    Garcia-Fernandez, Maria
    Alvarez-Narciandi, Guillermo
    Las-Heras Andres, Fernando
    [J]. IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE, 2022, 10 (02) : 66 - 86
  • [3] Annan, 2005, NEAR SURF GEOPHYS, P357, DOI [10.1190/1.9781560801719.ch11, DOI 10.1190/1.9781560801719.CH11]
  • [4] Brookes A., 2007, AUST J OUTDOOR ED, V11, P50, DOI DOI 10.1007/BF03400857
  • [5] MICROWAVE DIELECTRIC BEHAVIOR OF WET SOIL .2. DIELECTRIC MIXING MODELS
    DOBSON, MC
    ULABY, FT
    HALLIKAINEN, MT
    ELRAYES, MA
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 1985, 23 (01): : 35 - 46
  • [6] Real-Time Hyperbola Recognition and Fitting in GPR Data
    Dou, Qingxu
    Wei, Lijun
    Magee, Derek R.
    Cohn, Anthony G.
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2017, 55 (01): : 51 - 62
  • [7] Automatic reconstruction of three-dimensional root system architecture based on ground penetrating radar
    Fan, Guoqiu
    Liang, Hao
    Zhao, Yandong
    Li, Yinghang
    [J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2022, 197
  • [8] Hand-Held GPR Imaging Using Migration for Irregular Data
    Feng, Xuan
    Sato, Motoyuki
    Liu, Cai
    [J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2011, 4 (04) : 799 - 803
  • [9] Deep Residual Learning for Image Recognition
    He, Kaiming
    Zhang, Xiangyu
    Ren, Shaoqing
    Sun, Jian
    [J]. 2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, : 770 - 778
  • [10] Hu J, 2018, PROC CVPR IEEE, P7132, DOI [10.1109/CVPR.2018.00745, 10.1109/TPAMI.2019.2913372]