Filtering LiDAR data based on adjacent triangle of triangulated irregular network
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The filtering of LiDAR points cloud data is a fundamental procedure in the production of Digital Elevation Model. Against the lack of using the relationship between the adjacent terrain and the points to be judged in the point cloud filtering, a LiDAR points cloud data filtering algorithm based on adjacent triangles in TIN (Triangulated Irregular Network) is proposed. It utilizes the elevation information of each triangle’s adjacent triangles to detect the building edge points, and acquires the building points by region growing, then detects the isolated points with the morphological filtering algorithm, finally determines the ground point set and generates DEM. We evaluate the performance of the proposed method on the ISPRS LiDAR reference dataset. Experimental results show that the algorithm can effectively remove non-ground points, keep the ground points and minimize total error rates effectively while maintaining acceptable Type I and Type II error rates.
KeywordsLiDAR data TIN Adjacent triangle Region growing
The work was jointly supported by the National Natural Science Foundations of China under grant No. 61472302,61272280,U1404620,and 41271447; The Program for New Century Excellent Talents in University under grant No. NCET-12-0919; The Fundamental Research Funds for the Central Universities under grant No. K5051203020, K5051303018, JB150313,JB150317,and BDY081422,; Natural Science Foundation of Shaanxi Province, under grant No.2014JM8310 and, 2010JM8027; The Creative Project of the Science and Technology State of xi’an under grant No. CXY1441(1); The State Key Laboratory of Geo-information Engineering under grant No.SKLGIE2014-M-4-4.
- 1.Axelsson P (2000) DEM generation from laser scanner data using adaptive TIN models[J]. Int Arch Photogramm Remote Sens Spat Inf Sci 33(Part B4):110–117Google Scholar
- 3.Feng Y, Jixian Z, et al (2009) Urban DEM generation from airborne Lidar data[C]. Urban Remote Sensing Event, 2009 Joint. IEEE, pp 1–5Google Scholar
- 5.Han W, Li Y, Chen L (2012) High-precision DEM production in complex urban area using LiDAR data[C]. 2012 20th International Conference on Geoinformatics. IEEE, pp 1–5Google Scholar
- 6.Haugerud R, Harding DJ (2001) Some algorithms for virtual deforestation(VDF)of LIDAR topographic survey data[J]. Int Arch Photogramm Remote Senning Spat Inf Sci 34(W4):211–218Google Scholar
- 7.Kilian J, Haala N, Englich M (1996) Capture and evaluation of airborne laser scanner data. Int Arch photogramm Remote Sens Spat Inf Sci 31(Part B3):383–388Google Scholar
- 12.Shao L, Hu P, Huang C (2004) The expatiation of DELAUNAY algorithm and a promising direction in application[J]. Sci Surv Mapp 29(6):68–71 (in Chinese)Google Scholar
- 13.Sithole G (2005) Segmentation and classification of airborne laser scanner data[D]. International Institute for Geo-information Science and Earth Observation (ITC) the degree of Master, NetherlandsGoogle Scholar
- 15.Vosselman G (2000) Slope based filtering of laser altimetry data[J]. Int Arch Photogramm Remote Sens Spat Inf Sci 33(Part B3):935–942Google Scholar
- 16.Wang H, Zhang Y, Li P, Zha X (2013) A method of deriving dem from airborne lidar points cloud data[C].Urban Remonte Sensing Event (JURSE), Joint, pp, 013–016Google Scholar
- 17.Wu C, Lu X, Li G et al (2013) Research on filtering algorithm for LiDAR data based on TIN[J]. Bull Surv Mapp 3:32–35 (in Chinese)Google Scholar
- 18.Yu H, Lu X et al (2010) Digital terrain model extraction from airborne LiDARdata in complex mining area[C]. 2010 18th International Conference on Geoinformatics. IEEE, pp 1–6Google Scholar