An Efficient and Accurate Method for Different Configurations Railway Extraction Based on Mobile Laser Scanning
Abstract
:1. Introduction
2. Methodology
2.1. Data Preprocessing
2.2. Region Growing Estimation Method
2.2.1. Raster Filtering and Vector Shift Estimation
2.2.2. K-means Clustering
2.3. Reverse Smoothing
- The point of track turnout cannot be smoothed in reverse. The error of the rail region of the endpoint can be calculated to exclude the point of turnout being relocated.
- The density of point compensated by reverse smoothing is less than two adjacent point and .
- As shown in the Figure 7, the two groups that are adjacent endpoints of are selected as blue and green lines, respectively, and a new endpoint is fitted by circular curve on plane expressed in the Formula (5). Bring the plane coordinates of , , and into the Formula (5) to get a set of parameters of the circular curve. The same process fits another circular curve by , and, of the green line. Next, take into these two functions. is the average value of corresponding points in the two functions.If radius of the functions is greater than empirical threshold 3000m, they’re considered lines. The following is the calculation formula (6) of radius .
- Comparing the densities of clustering centers and , which is the same as the density mentioned in Section 2.2.2. If the density of the corrected endpoint is significantly greater than that of the original endpoint , the endpoint is corrected to .
3. Environments and Data Description
3.1. Data Description
3.2. Test Area Description
4. Results and Discussion
4.1. Results
4.2. Parameter Analysis
4.2.1. Strip Length
4.2.2. Raster Size
4.3. Quantitative Evaluation
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Parameter | Description | Values |
---|---|---|
LEN | Strip length (Straight-line) Strip length (Divided-deck) Strip length (bends) | 15 m 15 m 8 m |
R | Radius of pseudo-track point Raster size | 0.3 m 0.15 m |
INTEN | intensity threshold | 5 |
Method | Precision (%) | Sensitivity (%) |
---|---|---|
Kalman | 82.28 | 91.96 |
Region growing | 96.70 | 89.47 |
Scene | Precision (%) | Sensitivity (%) |
---|---|---|
Bends | 90.32 | 83.27 |
Turnouts | 81.31 | 83.33 |
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Zou, R.; Fan, X.; Qian, C.; Ye, W.; Zhao, P.; Tang, J.; Liu, H. An Efficient and Accurate Method for Different Configurations Railway Extraction Based on Mobile Laser Scanning. Remote Sens. 2019, 11, 2929. https://doi.org/10.3390/rs11242929
Zou R, Fan X, Qian C, Ye W, Zhao P, Tang J, Liu H. An Efficient and Accurate Method for Different Configurations Railway Extraction Based on Mobile Laser Scanning. Remote Sensing. 2019; 11(24):2929. https://doi.org/10.3390/rs11242929
Chicago/Turabian StyleZou, Rong, Xiaoyun Fan, Chuang Qian, Wenfang Ye, Peng Zhao, Jian Tang, and Hui Liu. 2019. "An Efficient and Accurate Method for Different Configurations Railway Extraction Based on Mobile Laser Scanning" Remote Sensing 11, no. 24: 2929. https://doi.org/10.3390/rs11242929