融合光谱信息的机载LiDAR点云三维深度学习分类方法 
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3D Deep Learning Classification Method for Airborne LiDAR Point Clouds Fusing Spectral Information
河南理工大学测绘与国土信息工程学院, 河南 焦作 454000
图 & 表
图 1. 基于PointNet的三维点云分类流程
Fig. 1. Classification flow of 3D point clouds based on PointNet
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图 2. 融合多光谱信息的机载LiDAR点云分类流程
Fig. 2. Flow chart of classification for airborne LiDAR point clouds fusing multi-spectral imagery
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图 3. 点云数据的多尺度格网化处理
Fig. 3. Multi-scale grid processing on point cloud data
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图 4. 训练数据集及其对应区域的多光谱影像。(a)训练数据集;(b)多光谱影像
Fig. 4. Training set and the corresponding multi-spectral imagery. (a) Training set; (b) multi-spectral imagery
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图 5. 测试数据集及其对应区域的多光谱影像。(a)测试数据集;(b)多光谱影像
Fig. 5. Test set and the corresponding multi-spectral imagery. (a) Test set; (b) multi-spectral imagery
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图 6. 未融合光谱信息的点云分类结果。(a)不同地物的分类结果;(b)错误分类结果
Fig. 6. Classification results of original LiDAR point clouds. (a) Classification results of different objects; (b) misclassification results
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图 7. 融合多光谱信息的点云分类结果。(a)不同地物的分类结果;(b)错误分类结果
Fig. 7. Classification results of multi-spectral point clouds. (a) Classification results of different objects; (b) misclassification results
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表 1不同尺度下点云分类精度对比
Table1. Comparison of classification accuracy under different scales
| Size /m | OA /% | Kappa |
|---|
| 2 | 74.88 | 0.6756 | | 5 | 81.54 | 0.7592 | | 10 | 81.14 | 0.7541 | | 15 | 78.36 | 0.7178 | | 2,5,10 | 80.00 | 0.7396 | | 5,10,15 | 82.02 | 0.7658 |
|
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表 2融合多光谱信息前、后的点云分类结果
Table2. Point cloud classification results of unfused and fused spectral information
| Type of data | F1 /% | OA /% | Kappa |
|---|
| | Low vegetation | Impervious surface | Car | Roof | Shrub | Tree |
|---|
| Original data | 73.60 | 83.28 | 35.88 | 69.06 | 37.01 | 51.86 | 68.63 | 0.5969 | | Fused data | 75.92 | 85.90 | 54.03 | 94.08 | 42.69 | 79.47 | 82.02 | 0.7658 |
|
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表 3不同分类方法的精度对比
Table3. Accuracy comparison of different classification methods
| Method | F1 /% |
|---|
| Low vegetation | Impervious surface | Car | Roof | Shrub | Tree |
|---|
| Ours | 75.9 | 85.9 | 54.0 | 94.1 | 42.7 | 79.5 | | IIS_7 | 65.2 | 85.0 | 57.9 | 90.9 | 39.5 | 75.6 | | UM | 79.0 | 89.1 | 47.7 | 92.0 | 40.9 | 77.9 | | NANJ | 77.7 | 90.9 | 51.7 | 93.6 | - | 77.1 |
|
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王宏涛, 雷相达, 赵宗泽. 融合光谱信息的机载LiDAR点云三维深度学习分类方法[J]. 激光与光电子学进展, 2020, 57(12): 122802. Hongtao Wang, Xiangda Lei, Zongze Zhao. 3D Deep Learning Classification Method for Airborne LiDAR Point Clouds Fusing Spectral Information[J]. Laser & Optoelectronics Progress, 2020, 57(12): 122802.
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