• Journal of Infrared and Millimeter Waves
  • Vol. 40, Issue 3, 381 (2021)
Xu-Dong LAI1、2, Yi-Fei YUAN1, Jing-Zhong XU1、*, and Ming-Wei WANG3
Author Affiliations
  • 1School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
  • 2Key Laboratory of National Geographic Census and Monitoring, Ministry of Natural Resources, Wuhan 430079, China
  • 3Institute of Geological Survey, China University of Geosciences, Wuhan 430074, China
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    DOI: 10.11972/j.issn.1001-9014.2021.03.015 Cite this Article
    Xu-Dong LAI, Yi-Fei YUAN, Jing-Zhong XU, Ming-Wei WANG. LiDAR waveform decomposition based on modified differential evolution algorithm[J]. Journal of Infrared and Millimeter Waves, 2021, 40(3): 381 Copy Citation Text show less
    Schematic diagram of chaos algorithm
    Fig. 1. Schematic diagram of chaos algorithm
    Waveform decomposition flowchart combined with MDE algorithm
    Fig. 2. Waveform decomposition flowchart combined with MDE algorithm
    The first set of waveform fitting results (a) non-optimization, (b) LM optimization based on GGM, (c) L-BFGS optimization based on GGM, (d) DE optimization based on GGM, (e) MDE optimization based on GM, (f) MDE optimization based on GGM
    Fig. 3. The first set of waveform fitting results (a) non-optimization, (b) LM optimization based on GGM, (c) L-BFGS optimization based on GGM, (d) DE optimization based on GGM, (e) MDE optimization based on GM, (f) MDE optimization based on GGM
    The second set of waveform fitting result (a) non-optimization, (b) LM optimization based on GGM, (c) L-BFGS optimization based on GGM, (d) DE optimization based on GGM, (e) MDE optimization based on GM, (f) MDE optimization based on GGM
    Fig. 4. The second set of waveform fitting result (a) non-optimization, (b) LM optimization based on GGM, (c) L-BFGS optimization based on GGM, (d) DE optimization based on GGM, (e) MDE optimization based on GM, (f) MDE optimization based on GGM
    The third set of waveform fitting results (a) non-optimization, (b) LM optimization based on GGM, (c) L-BFGS optimization based on GGM, (d) DE optimization based on GGM, (e) MDE optimization based on GM, (f) MDE optimization based on GGM
    Fig. 5. The third set of waveform fitting results (a) non-optimization, (b) LM optimization based on GGM, (c) L-BFGS optimization based on GGM, (d) DE optimization based on GGM, (e) MDE optimization based on GM, (f) MDE optimization based on GGM
    Study area
    Fig. 6. Study area
    Point cloud of waveform decomposition
    Fig. 7. Point cloud of waveform decomposition
    Comparison of system and decomposed point cloud (a) system point cloud(red) and decomposed point cloud(white), (b) new point cloud after decomposition
    Fig. 8. Comparison of system and decomposed point cloud (a) system point cloud(red) and decomposed point cloud(white), (b) new point cloud after decomposition
    Point cloud of flat roof (a)system, (b) LM, (c) L-BFGS, (d) DE, (e) MDE
    Fig. 9. Point cloud of flat roof (a)system, (b) LM, (c) L-BFGS, (d) DE, (e) MDE
    Point cloud of gabled roof (a)system, (b) LM, (c) L-BFGS, (d) DE, (e) MDE
    Fig. 10. Point cloud of gabled roof (a)system, (b) LM, (c) L-BFGS, (d) DE, (e) MDE
    优化算法不优化LML-BFGSGAPSODEMDE
    误差160.462 168.968 566.076 1101.773 698.054 965.562 559.867 1
    时间/s0.857 2319.355 6129.689 0320.503 0265.888 0237.773 0293.809 0
    Table 1. Fitting accuracy and running time of different optimization algorithms
    系统LML-BFGSDEMDE
    平顶/cm6.668 98.779 37.261 26.874 26.627 1
    人字形屋顶/cm8.855 19.582 08.682 98.352 68.531 1
    Table 2. Roof fitting error of each algorithm
    Xu-Dong LAI, Yi-Fei YUAN, Jing-Zhong XU, Ming-Wei WANG. LiDAR waveform decomposition based on modified differential evolution algorithm[J]. Journal of Infrared and Millimeter Waves, 2021, 40(3): 381
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