Chinese Journal of Lasers, Volume. 48, Issue 16, 1610001(2021)
Small-Sample Airborne LiDAR Point Cloud Classification Based on Transfer Learning and Fully Convolutional Network
Figures & Tables(15)
Fig. 1. Flow chart of the small sample point cloud classification based on transfer learning
Fig. 2. Generation process of the point feature map. (a) Two-dimensional coordinates of the feature map; (b) point features in the cube neighborhood; (c) point feature map
Fig. 3. Generation of the multi-scale feature maps. (a) Grid size of 0.1 m; (b) grid size of 0.3 m; (c) grid size of 0.5 m
Fig. 4. Schematic diagram of the multi-projection. (a) X direction; (b) Y direction
Fig. 7. Experimental datasets. (a) Training dataset displayed by normalized height; (b) aerial image corresponding to training dataset; (c) testing dataset displayed by normalized height; (d) aerial image corresponding to testing dataset
Fig. 11. Comparison of the misclassification results. (a) Misclassification result before graph-cuts optimization; (b) misclassification result after graph-cuts optimization
Table 1. Influence of different K on the classification results unit: %
View tableTable 1. Influence of different K on the classification results unit: %
K F1 score OA Avg F1 Low_veg Imp_sur Car Fe/He Roof Facade Shrub Tree 0 86.11 93.51 66.24 65.77 94.46 61.09 68.75 89.08 87.76 78.13 1 86.50 93.64 68.49 67.12 94.69 62.05 69.72 89.43 88.18 78.96 2 87.14 94.15 75.89 68.96 95.30 64.57 71.19 89.99 89.12 80.90 3 87.15 93.99 78.44 71.00 95.76 65.91 72.60 90.69 89.60 81.94 4 87.42 94.09 79.91 71.78 96.06 67.49 72.04 90.80 89.91 82.45 5 87.15 93.69 79.62 70.66 96.24 67.23 70.73 90.69 89.76 82.00 6 86.58 93.19 73.60 70.22 96.03 65.22 68.44 90.53 89.27 80.48
Table 2. F1 scores and overall classification accuracy of different methods unit: %
View tableTable 2. F1 scores and overall classification accuracy of different methods unit: %
Method F1 score OA Pow Low_veg Imp_surf Car Fe/He Roof Facade Shrub Tree UM 46.1 79.0 89.1 47.7 5.2 92.0 52.7 40.9 77.9 80.8 WhuY2 31.9 80.0 88.9 40.8 24.5 93.1 49.4 41.1 77.3 81.0 WhuY3 37.1 81.4 90.1 63.4 23.9 93.4 47.5 39.9 78.0 82.3 LUH 59.6 77.5 91.1 73.1 34.0 94.2 56.3 46.6 83.1 81.6 BIJ_W 13.8 78.5 90.5 56.4 36.3 92.2 53.2 43.3 78.4 81.5 RIT_1 37.5 77.9 91.5 73.4 18.0 94.0 49.3 45.9 82.5 81.6 NANJ2 62.0 88.8 91.2 66.7 40.7 93.6 42.6 55.9 82.6 85.2 WhuY4 42.5 82.7 91.4 74.7 53.7 94.3 53.1 47.9 82.8 84.9 Ours -- 87.4 94.1 79.9 71.8 96.1 67.5 72.0 90.8 89.9
Table 3. Classification results of our method and NANJ2 method unit: %
View tableTable 3. Classification results of our method and NANJ2 method unit: %
Method F1 score OA Avg F1 Low_veg Imp_surf Car Fe/He Roof Facade Shrub Tree NANJ2 86.9 93.1 69.5 70.9 95.2 73.0 65.3 92.1 89.1 80.8 Ours 88.6 93.8 68.7 75.5 95.6 74.0 68.8 94.3 90.1 82.4
Table 4. Classification results of different transfer learning methods unit: %
View tableTable 4. Classification results of different transfer learning methods unit: %
Method F1 score OA Avg F1 Low_veg Imp_surf Car Fe/He Roof Facade Shrub Tree DRN 83.3 92.5 52.4 62.5 95.2 62.8 64.9 88.7 86.8 75.3 DRN-1 87.0 94.3 42.2 65.7 96.0 66.0 71.7 91.8 89.5 78.2 Ours 87.4 94.1 79.9 71.8 96.1 67.5 72.0 90.8 89.9 82.4
Tools
Get Citation
Copy Citation Text
Xiangda Lei, Hongtao Wang, Zongze Zhao. Small-Sample Airborne LiDAR Point Cloud Classification Based on Transfer Learning and Fully Convolutional Network[J]. Chinese Journal of Lasers, 2021, 48(16): 1610001
Paper Information
Category: remote sensing and sensor
Received: Nov. 28, 2020
Accepted: Feb. 7, 2021
Published Online: Jul. 30, 2021
The Author Email: Hongtao Wang (211804010013@home.hpu.edu.cn)
© Copyright 2018-2021 | Chinese Laser Press.
All Rights Reserved 沪ICP备15018463号-20