一种改进的适用于DIM点云的PTD滤波算法
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摘要
提出一种改进的适用于三维密集图像匹配(DIM)点云的渐近不规则三角网加密滤波算法(PTD).该算法针对DIM点云无法穿透植被、直达地面的特性,在种子点选取阶段,采用一种基于建筑物立面的种子点选取策略选取种子点.采用PTD算法的加密策略对由种子点构建的不规则三角网(TIN)进行迭代加密.当归属于网内的点的密度大于所给阈值时,所提算法采用一种新的基于多尺度的迭代加密方案对TIN继续加密.与PTD算法不同,在第二个加密阶段内,加密角度阈值将从小到大变化.结果表明:改进的PTD算法可有效地将地面点和非地面点分离,点云滤波的Ⅰ类误差、Ⅱ类误差及总误差分别为3.57%,0.71%和1.68%.算法对DIM点云的滤波效果优于PTD算法,因此,该算法将成为处理DIM点云的重要工具之一.
This paper proposed an improved PTD filtering algorithm for DIM point clouds.First,a new strategy of seed points selection based on the fa ades of the building is adopted by this method aimed at the fact that the DIM point clouds cannot penetrate into the vegetation and touch to the ground in the stage of seed point selection.Then,this method makes use of the iterative densification strategy of the PTD algorithm to densify the TIN constructed by the seed points.When the density of the points belonging to TIN is greater than the given threshold,a new iterative densification scheme based on multi-scale is used to continue to densify the TIN.The difference between the improved method and PTD method is the change of densification angle threshold which is from small to large during the second densification phase.The experimental results show that the improved PTD algorithm can effectively separate the ground and non-ground points;and the typeⅠerror,typeⅡerror and total error is 3.57%,0.71%and 1.68%,respectively.The performance of our improved method for DIM point clouds is better than that of the PTD algorithm;hence,this method can be one of important tools for DIM point clouds.
This paper proposed an improved PTD filtering algorithm for DIM point clouds.First,a new strategy of seed points selection based on the fa ades of the building is adopted by this method aimed at the fact that the DIM point clouds cannot penetrate into the vegetation and touch to the ground in the stage of seed point selection.Then,this method makes use of the iterative densification strategy of the PTD algorithm to densify the TIN constructed by the seed points.When the density of the points belonging to TIN is greater than the given threshold,a new iterative densification scheme based on multi-scale is used to continue to densify the TIN.The difference between the improved method and PTD method is the change of densification angle threshold which is from small to large during the second densification phase.The experimental results show that the improved PTD algorithm can effectively separate the ground and non-ground points;and the typeⅠerror,typeⅡerror and total error is 3.57%,0.71%and 1.68%,respectively.The performance of our improved method for DIM point clouds is better than that of the PTD algorithm;hence,this method can be one of important tools for DIM point clouds.
引文
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[2]DAHLKE D,LINKIEWICZ M,MEISSNER H.True3Dbuilding reconstruction:fa9ade,roof and overhang modelling from oblique and vertical aerial imagery[J].International Journal of Image and Data Fusion,2015,6(4):314-329.
[3]LI Z,GRUEN A.Automatic DSM generation from linear array imagery data[J].International Archives of Photogrammetry,Remote Sensing and Spatial Information Sciences,2004,35(B3):128-133.
[4]SITHOLE G,VOSSELMAN G.Experimental comparison of filter algorithms for Bare-Earth extraction from airborne laser scanning point clouds[J].ISPRSJournal of Photogrammetry and Remote Sensing,2004,59(1):85-101.
[5]AXELSSON P.Processing of laser scanner data-algorithms and applications[J].ISPRS Journal of Photogrammetry and Remote Sensing,1999,54:138-147.
[6]CAVEGN S,HAALA N,NEBIKER S,et al.Benchmarking high density image matching for oblique airborne imagery[J].ISPRS International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences,2014,40(3):45-52.
[7]YILMAZ C S,GUNGOR O.Comparison of the performances of ground filtering algorithms and DTMgeneration from a UAV-based point cloud[J].Geocarto International,2016,32(5):1-41.
[8]ZHANG K,CHEN S C,WHITMAN D,et al.A progressive morphological filter for removing non-ground measurements from airborne LIDAR data[J].IEEETransactions on Geoscience&Remote Sensing,2003,41(4):872-882.
[9]VOSSELMAN G.Slope based filtering of laser altimetry data[J].International Archives of Photogrammetry&Remote Sensing,2000,33:935-942.
[10]WHITMAN D,ZHANG K.Comparison of three algorithms for filtering airborne lidar data[J].Photogrammetric Engineering&Remote Sensing,2005,71(3):313-324.
[11]ZHANG Z,GERKE M,VOSSELMAN G,et al.Filtering photogrammetric point clouds using standard lidar filters towards DTM generation[J].ISPRS Annals of Photogrammetry,Remote Sensing and Spatial Information Sciences,2018,2:319-326.
[12]DONG Y Q,ZHANG L,CUI X M,et al.An automatic filter algorithm for dense image matching point clouds[J].ISPRS International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences,2017,2:703-709.
[13]MEYER G E,NETO J C.Verification of color vegetation indices for automated crop imaging applications[J].Computers and Electronics in Agriculture,2008,63(2):282-293.
[14]史文中,李必军,李清泉.基于投影点密度的车载激光扫描距离图像分割方法[J].测绘学报,2005,34(2):95-100.SHI Wenzhong,LI Bijun,LI Qingquan.A method for segmentation of range image captured in vehicleborne lasers canning based on the density of projected points[J].Acta Geodaetica et Cartographica Sinica,2005,34(2):95-100.