Acta Photonica Sinica, Volume. 50, Issue 1, 188(2021)
Registration of Laser Point Cloud and Optical Image in Urban Area Based on Semantic Segmentation
Figures & Tables(19)
Fig. 3. The segmentation results of UNET model for different kind image
Fig. 4. The outline of buildings(blue) and the minimum enclosing rectangle(red) from the segmentation result
Fig. 7. The first comparative experiment of optical image segmentation results between traditional segmentation method and deep learning method
Fig. 8. The second comparative experiment of optical image segmentation results between traditional segmentation method and deep learning method
Fig. 9. The first group of comparative experiments of point cloud segmentation results between traditional segmentation method and deep learning method
Fig. 10. The second group of comparative experiments of point cloud segmentation results between traditional segmentation method and deep learning method
Table 1. Segmentation index data
View tableView in ArticleTable 1. Segmentation index data
Data Segment method The first optical image Unet 94.93% 11.22% 84.76% Meanshift clustering segmentation 85.06% 14.19% 74.58% Maximum entropy threshold segmentation 85.56% 8.53% 79.24% The second optical image Unet 99.65% 11.82% 87.91% Meanshift clustering segmentation 78.71% 34.17% 55.88% Maximum entropy threshold segmentation 80.46% 6.08% 76.48% The first point cloud Unet 95.67% 10.74% 86.80% Meanshift clustering segmentation 25.19% 49.53% 20.20% Maximum entropy threshold segmentation 22.29% 56.72% 17.25% The second point cloud Unet 99.60% 14.62% 85.08% Meanshift clustering segmentation 20.28% 3.91% 20.11% Maximum entropy threshold segmentation 22.29% 56.72% 17.25%
Table 2. Registration accuracy（uint:pixel）
View tableView in ArticleTable 2. Registration accuracy（uint:pixel）
Data Min error Max error Average error The first group
of test data
a 1.41 4.47 2.85 b 2.23 5.38 3.05 c 2.23 5.83 3.51 d 1.00 3.61 2.14 e 1.00 3.00 2.27 The second group
of test data
a 2.23 6.06 4.05 b 4.47 5.00 4.73 c 2.00 3.60 2.88 d 1.07 4.24 2.76 e 1.41 5.65 3.51 The third group
of test data
a 1.24 4.00 2.71 b 2.00 2.82 2.47 c 2.00 5.09 3.27 d 2.23 6.08 3.98 e 1.00 4.00 2.39
Table 3. The matching of feature points of method II, method III and our method. And the root mean square error of four registration methods
View tableView in ArticleTable 3. The matching of feature points of method II, method III and our method. And the root mean square error of four registration methods
Data Method I Method II Method III Our method E Match points Correct match E Match points Correct match E Match points Correct match E a-1 9.83 9 2 / 4 0 / 5 5 3.16 a-2 10.21 18 2 / 7 4 4.08 4 4 3.93 a-3 8.14 20 2 / 5 0 / 3 3 3.75 a-4 10.18 25 1 / 5 1 / 5 5 2.78 a-5 15.07 18 3 2.65 5 2 / 4 4 2.38 b-1 74.79 19 0 / 6 0 / 4 4 4.55 b-2 191.55 17 0 / 5 2 / 5 5 4.74 b-3 191.28 13 0 / 6 0 / 5 5 2.95 b-4 88.64 13 0 / 6 1 / 5 5 3.34 b-5 138.46 12 0 / 4 1 / 5 5 3.51 c-1 115.18 14 1 / 4 1 / 4 4 2.94 c-2 154.13 26 0 / 6 2 / 4 4 2.49 c-3 124.37 17 0 / 6 0 / 3 3 3.42 c-4 107.55 9 0 / 7 4 4.41 4 4 4.30 c-5 149.23 13 1 / 6 0 / 4 4 2.70
Tools
Get Citation
Copy Citation Text
Ying ZHU, Ming ZHAO. Registration of Laser Point Cloud and Optical Image in Urban Area Based on Semantic Segmentation[J]. Acta Photonica Sinica, 2021, 50(1): 188
Paper Information
Category: Image Processing
Received: --
Accepted: --
Published Online: Mar. 12, 2021
The Author Email:
© Copyright 2018-2021 | Chinese Laser Press.
All Rights Reserved 沪ICP备15018463号-20