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Extraction of ground points from LiDAR data based on slope and progressive window thresholding (SPWT) | ||
| Earth Observation and Geomatics Engineering | ||
| مقاله 4، دوره 2، شماره 1، شهریور 2018، صفحه 36-44 اصل مقاله (991.83 K) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22059/eoge.2018.240284.1012 | ||
| نویسندگان | ||
| Pejman Rashidi1؛ Heidar Rastiveis* 2 | ||
| 1School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, | ||
| 2School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran | ||
| چکیده | ||
| Filtering of airborne LiDAR point clouds has broad applications, such as Digital Terrain Model (DTM) generation and three-dimensional urban modeling. Although several methods have been developed to separate the point clouds into ground and non-ground points, there are some challenges to identify the complex objects such as bridge and eccentric roofs. In this study, a new algorithm based on the Slope and Progressive Window Thresholding (SPWT) is proposed for ground filtering of LiDAR data. This algorithm is based on both multi-scale and slope methods that have strong effects on filtering the LiDAR data. The proposed algorithm utilizes the slope between adjacent points and the elevation information of points in a local window to detect non-ground objects. Therefore, not only it benefits from vertical information in each local window to detect the non-ground points, but it also uses the neighbor information in directional scanning, and it prevents the errors introduced by the sensitivity to direction. According to the physical characteristics of the ground surface and the size of objects, the best threshold values are considered. In order to evaluate the performance of the SPWT method, both low and high resolution datasets were applied that their average overall accuracy were reported to be 94.21% and 93.08%, respectively. These results proved that, irrespective of data resolution, the SPWT method could effectively remove the non-ground points from airborne LiDAR data. | ||
| کلیدواژهها | ||
| Ground Filtering؛ LiDAR؛ Points Cloud؛ DEM Generation | ||
| مراجع | ||
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Anderson, E. S., Thompson, J. A., & Austin, R. E. (2005). LIDAR density and linear interpolator effects on elevation estimates. International Journal of Remote Sensing, 26(18), 3889-3900. Arefi, H., & Hahn, M. (2005). A morphological reconstruction algorithm for separating off-terrain points from terrain points in laser scanning data. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36(3/W19), 120-125. Axelsson, P. (2000). DEM generation from laser scanner data using adaptive TIN models. International archives of photogrammetry and remote sensing, 33(4), 110-117. Bretar, F., & Chehata, N. (2010). Terrain modeling from lidar range data in natural landscapes: A predictive and bayesian framework. IEEE Transactions on Geoscience and Remote Sensing, 48(3), 1568-1578. Chen, D., Zhang, L., Wang, Z., & Deng, H. (2013). A mathematical morphology-based multi-level filter of LiDAR data for generating DTMs. Science China Information Sciences, 56(10), 1-14. Chen, Q., Gong, P., Baldocchi, D., & Xie, G. (2007). Filtering airborne laser scanning data with morphological methods. Photogrammetric Engineering & Remote Sensing, 73(2), 175-185. Chen, Z., Devereux, B., Gao, B., & Amable, G. (2012). Upward-fusion urban DTM generating method using airborne Lidar data. ISPRS journal of photogrammetry and remote sensing, 72, 121-130. Chen, Z., Gao, B., & Devereux, B. (2017). State-of-the-art: DTM generation using airborne LIDAR data. Sensors, 17(1), 150. Eckstein, W., & Muenkelt, O. (1995, August). Extracting objects from digital terrain models. In Remote sensing and reconstruction for three-dimensional objects and scenes (Vol. 2572, pp. 43-52). International Society for Optics and Photonics. Hui, Z., Hu, Y., Yevenyo, Y. Z., & Yu, X. (2016). An improved morphological algorithm for filtering airborne LiDAR point cloud based on multi-level kriging interpolation. Remote Sensing, 8(1), 35. Kobler, A., Pfeifer, N., Ogrinc, P., Todorovski, L., Oštir, K., & Džeroski, S. (2007). Repetitive interpolation: A robust algorithm for DTM generation from Aerial Laser Scanner Data in forested terrain. Remote sensing of environment, 108(1), 9-23. Li, Y. (2013). Filtering airborne LiDAR data by an improved morphological method based on multi-gradient analysis. Int. Arch. Photogr., Remote Sens. Spatial Inf. Sci, 40, 191-194. Li, Y., Wu, H., Xu, H., An, R., Xu, J., & He, Q. (2013). A gradient-constrained morphological filtering algorithm for airborne LiDAR. Optics & Laser Technology, 54, 288-296. Li, Y., Yong, B., Wu, H., An, R., & Xu, H. (2014). An improved top-hat filter with sloped brim for extracting ground points from airborne lidar point clouds. Remote Sensing, 6(12), 12885-12908. Meng, X., Currit, N., & Zhao, K. (2010). Ground filtering algorithms for airborne LiDAR data: A review of critical issues. Remote Sensing, 2(3), 833-860. Meng, X., Wang, L., Silván-Cárdenas, J. L., & Currit, N. (2009). A multi-directional ground filtering algorithm for airborne LIDAR. ISPRS Journal of Photogrammetry and Remote Sensing, 64(1), 117-124. Okagawa, Masaomi. 2001. "Algorithm of multiple filter to extract DSM from LiDAR data.", 2001 ESRI International User Conference, 193-203. Pingel, T. J., Clarke, K. C., & McBride, W. A. (2013). An improved simple morphological filter for the terrain classification of airborne LIDAR data. ISPRS Journal of Photogrammetry and Remote Sensing, 77, 21-30. Quan, Y., Song, J., Guo, X., Miao, Q., & Yang, Y. (2017). Filtering LiDAR data based on adjacent triangle of triangulated irregular network. Multimedia Tools and Applications, 76(8), 11051-11063. Rashidi, P., & Rastiveis, H. (2017). Ground Filtering LiDAR Data Based on Multi-Scale Analysis of Height Difference Threshold. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 225-229. Shan, J., & Aparajithan, S. (2005). Urban DEM generation from raw LiDAR data. Photogrammetric Engineering & Remote Sensing, 71(2), 217-226. Sithole, G., & Vosselman, G. (2001). Filtering of laser altimetry data using a slope adaptive filter. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, 34(3/W4), 203-210. Sithole, G., & Vosselman, G. (2003). Report: ISPRS Comparison of filters. Department of Geodesy, Faculty of Civil Engineering and GeoSciences. Delft University of Technology. Sithole, G., & Vosselman, G. (2004). Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds. ISPRS journal of photogrammetry and remote sensing, 59(1-2), 85-101. Susaki, J. (2012). Adaptive slope filtering of airborne LiDAR data in urban areas for digital terrain model (DTM) generation. Remote Sensing, 4(6), 1804-1819. Uysal, M., & Polat, N. (2014). Investigating performance of airborne lidar data filtering with triangular irregular network (tin) algorithm. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(7), 199. Vosselman, G. (2000). Slope based filtering of laser altimetry data. International Archives of Photogrammetry and Remote Sensing, 33(B3/2; PART 3), 935-942. Wang, C. K., & Tseng, Y. H. (2010). DEM generation from airborne LiDAR data by an adaptive dual-directional slope filter. na. White, S. A., & Wang, Y. (2003). Utilizing DEMs derived from LIDAR data to analyze morphologic change in the North Carolina coastline. Remote sensing of environment, 85(1), 39-47. Zhang, J., & Lin, X. (2013). Filtering airborne LiDAR data by embedding smoothness-constrained segmentation in progressive TIN densification. ISPRS Journal of photogrammetry and remote sensing, 81, 44-59. Zhang, K., Chen, S. C., Whitman, D., Shyu, M. L., Yan, J., & Zhang, C. (2003). A progressive morphological filter for removing nonground measurements from airborne LIDAR data. IEEE transactions on geoscience and remote sensing, 41(4), 872-882. Zhang, K., & Whitman, D. (2005). Comparison of three algorithms for filtering airborne lidar data. Photogrammetric Engineering & Remote Sensing, 71(3), 313-324. Zhang, W., Qi, J., Wan, P., Wang, H., Xie, D., Wang, X., & Yan, G. (2016). An easy-to-use airborne LiDAR data filtering method based on cloth simulation. Remote Sensing, 8(6), 501. | ||
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