Tianye Xu, Haiyong Ding. Deep Learning Point Cloud Classification Method Based on Fusion Graph Convolution[J]. Laser & Optoelectronics Progress, 2022, 59(2): 0228005
- Laser & Optoelectronics Progress
- Vol. 59, Issue 2, 0228005 (2022)
Fig. 1. Structure of the deep learning classification network
Fig. 2. Principle of the spatial domain graph convolution
Fig. 3. Schematic diagram of the KNN
Fig. 4. Central node information aggregated under different orders. (a) q=1; (b) q=2
Fig. 5. Training data and multispectral aerial image of corresponding region. (a) Point cloud; (b) aerial image
Fig. 6. Testing data and multispectral aerial images of corresponding region. (a) Point cloud; (b) aerial image
Fig. 7. Fusion result of point cloud data and spectral images. (a) Training data; (b) testing data
Fig. 8. Schematic diagram of multiscale sampling
Fig. 9. Testing data labels and classification results. (a) True label; (b) classification result of our method
Fig. 10. Error map of the classification result
Fig. 11. Error maps of classification results of different methods. (a) PointNet; (b) DGCNN; (c) PointNet++; (d) our method
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Table 1. Number of various classes points in Vaihingen data set
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Table 2. Confusion matrix and evaluation indexes of the classification result
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Table 3. Quantitative evaluation index of different methods
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Table 4. Classification results of our method and existing methods
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