• Laser & Optoelectronics Progress
  • Vol. 57, Issue 8, 081019 (2020)
Xiaosong Shi*, Yinglei Cheng, Doudou Xue, and Xianxiang Qin
Author Affiliations
  • Information and Navigation College, Air Force Engineering University, Xi'an, Shaanxi 710077, China
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    DOI: 10.3788/LOP57.081019 Cite this Article Set citation alerts
    Xiaosong Shi, Yinglei Cheng, Doudou Xue, Xianxiang Qin. Object Classification Method for Multi-Source Fusion Point Clouds Based on Point-Net[J]. Laser & Optoelectronics Progress, 2020, 57(8): 081019 Copy Citation Text show less
    Flow chart of proposed method
    Fig. 1. Flow chart of proposed method
    Process of registration and fusion
    Fig. 2. Process of registration and fusion
    Architecture of Point-Net
    Fig. 3. Architecture of Point-Net
    Architecture of multi-scale Point-Net
    Fig. 4. Architecture of multi-scale Point-Net
    Simulation data. (a) Point cloud data; (b) remote-sensing image data; (c) overlapping image
    Fig. 5. Simulation data. (a) Point cloud data; (b) remote-sensing image data; (c) overlapping image
    Binary images of point clouds and remote-sensing image, results of registration, and fusion point clouds. (a) Binary image of point clouds; (b) binary image of remote-sensing image; (c) registration result; (d) fusion point cloud data
    Fig. 6. Binary images of point clouds and remote-sensing image, results of registration, and fusion point clouds. (a) Binary image of point clouds; (b) binary image of remote-sensing image; (c) registration result; (d) fusion point cloud data
    Training error curve
    Fig. 7. Training error curve
    Classification results. (a)(b) Without RGB point clouds; (c)(d) with RGB point clouds
    Fig. 8. Classification results. (a)(b) Without RGB point clouds; (c)(d) with RGB point clouds
    AlgorithmCategoryF1 /%Overall accuracy /%
    Ground79.2
    SVMVegetation81.278.7
    Building78.5
    Artificiality45.1
    Ground89.4
    Point-NetVegetation87.484.7
    Building78.2
    Artificiality24.2
    Multi-scalePoint-Netwithout RGBGround92.7
    Vegetation91.692.5
    Building93.3
    Artificiality57.1
    Multi-scalePoint-Netwith RGBGround98.5
    Vegetation97.696.8
    Building96.1
    Artificiality76.9
    Table 1. Classification accuracy of different methods
    MethodPoint-NetMulti-scale Point-Net without RGBMulti-scale Point-Net with RGB
    Training time437546495568
    Test time204.9209.8204.9
    Table 2. Classification time of different methodss
    Xiaosong Shi, Yinglei Cheng, Doudou Xue, Xianxiang Qin. Object Classification Method for Multi-Source Fusion Point Clouds Based on Point-Net[J]. Laser & Optoelectronics Progress, 2020, 57(8): 081019
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