平原地区机载激光雷达数据的抽稀算法分析
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摘要
目前,机载激光雷达点云数据在测绘行业中的应用还存在较多的瓶颈。为了使机载激光雷达点云数据更好地服务等值线等数据的生产,发挥其高效和高精度的优势,本文归纳、总结了国内外现有的LiDAR点云数据抽稀算法,并通过对比分析现有LiDAR点云数据抽稀算法存在的优缺点,如系统抽稀、格网抽稀、TIN抽稀和坡度抽稀等算法,结合平原地区激光点云在实际生产中的应用,研究了更适合平原地区点云数据的抽稀方法,通过大量的数据测试和试生产。结果表明,该方法可以在应用项目精度约束下保证数据质量,减少了后期数据处理应用的难度,提升了后续成果数据的质量,提高了作业生产效率,对机载激光雷达点云数据在测绘行业中的应用推广具有重要的现实意义。
At present,there are still many bottlenecks in the application of airborne LiDAR point cloud data in the surveying and mapping industry. In order to make airborne LiDAR point cloud data better serve the production of equivalent data,and give play to its advantages of high efficiency and high precision. In this paper,we summarize the existing algorithm of cloud data thinning in LiDAR at home and abroad,and analyze the advantages and disadvantages of the existing algorithm,such as system thinning,grid thinning,TIN thinning and slope thinning. Based on the application of laser point cloud in actual production in plain area,the method of thinning data more suitable for plain area is studied. Through a lot of data testing and trial production,the results show that this method can guarantee data quality under the constraints of application project accuracy. It reduces the difficulty of late data processing,improves the quality of the follow-up data,and improves the efficiency of the operation. It has a very important practical significance for the application and promotion of airborne Lidar cloud data in the surveying and mapping industry.
At present,there are still many bottlenecks in the application of airborne LiDAR point cloud data in the surveying and mapping industry. In order to make airborne LiDAR point cloud data better serve the production of equivalent data,and give play to its advantages of high efficiency and high precision. In this paper,we summarize the existing algorithm of cloud data thinning in LiDAR at home and abroad,and analyze the advantages and disadvantages of the existing algorithm,such as system thinning,grid thinning,TIN thinning and slope thinning. Based on the application of laser point cloud in actual production in plain area,the method of thinning data more suitable for plain area is studied. Through a lot of data testing and trial production,the results show that this method can guarantee data quality under the constraints of application project accuracy. It reduces the difficulty of late data processing,improves the quality of the follow-up data,and improves the efficiency of the operation. It has a very important practical significance for the application and promotion of airborne Lidar cloud data in the surveying and mapping industry.
引文
[1] AGARWAL P K,ARGE L,DANNER A. From point cloud to grid DEM:a scalable approach[C]∥Proceedings of the 12th International Symposium on Spatial Data Handling. Zurich:[s. n.],2006:771-788.
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[3]郭波,黄先锋,张帆,等.顾及空间上下文关系的JointBoost点云分类及特征降维[J].测绘学报,2013,42(5):715-721.
[4]邹长江,马洪超,张良,等.机载LIDAR点云时间纹理信息航带重叠区消冗[J].遥感学报,2014,18(6):1208-1216.
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[8]蔡先华,郑天栋.数字高程模型数据压缩及算法研究[J].测绘通报,2003(12):16-18.
[9]徐景中,万幼川,张圣望. Li DAR地面点云的简化方法研究[J].测绘信息与工程,2008,33(1):32-34.
[10] GAO J L. 2D and 3D surface image processing algorithms and their application[D]. New Jersey:New Jersey Institute of Technology,2001:17-18.
[11]缪志修,齐华,王国昌,等.基于机载Li DAR数据构建的DEM抽稀算法研究[J].铁道勘察,2010(4):35-40.
[12]刘春,吴杭彬.基于平面不规则三角网的DEM数据压缩与质量分析[J].中国图象图形学报,2007,12(5):836-840.
[13]缪志修.基于机载Li DAR数据的DEM抽稀算法研究[D].成都:西南交通大学,2010:24-28.
[14] PAMELA S. A data density education algorithm for postprocessed air-borne Li DAR bathymetric survey data[D]. Florida:University of Florida,2000.
[15]杜浩,朱俊锋,张力,等.顾及地形特征的Li DAR点云数据抽稀算法[J].测绘科学,2016,41(9):140-146.
[16] CHOU Y H,LIU P S,DEZZANI R J.Terrain complexity and reduction of topographic data[J]. Geographical Systems,1999,1(2):179-197.
[17]胡诚.精度约束下地表Li DAR点云抽稀方法研究[D].成都:西南交通大学,2015:71-76.
[18] SONG H,FENG H Y. A point cloud simplification algorithm for mechanical part inspection[C]∥Information Technology for Balanced Manufacturing Systems.Boston,MA:Springer,2006:461-468.
[19]周启鸣,刘学军.数字地形分析[M].北京:科学出版社,2006:217-219.
[2] HAMMOUDI K,DORNAIKA F,SOHEILIAN B,et al.Extracting wire-frame models of street facades from 3D joint clouds and the corresponding cadastral map[J].Remote Sensing and Spatial Information Sciences,2010,38(3A):91-96.
[3]郭波,黄先锋,张帆,等.顾及空间上下文关系的JointBoost点云分类及特征降维[J].测绘学报,2013,42(5):715-721.
[4]邹长江,马洪超,张良,等.机载LIDAR点云时间纹理信息航带重叠区消冗[J].遥感学报,2014,18(6):1208-1216.
[5] LIU X,ZHANG Z,PETERSON J,et al. The effect of Li DAR data density on DEM accuracy[C]∥Proceedings of the International Congress on Modelling&Simulation, Christchurch. New Zealand:[s. n.],2007:1363-1369.
[6]张小红.机载激光雷达测量技术理论与方法[M].武汉:武汉大学出版社,2007:9-14.
[7] ANDERSON E S,THOMPSON J A,AUSTIN R E,et al.Li DAR density and linear interpolator effects on elevation estimates[J]. International Journal of Remote Sensing,2005,26(18):3889-3900.
[8]蔡先华,郑天栋.数字高程模型数据压缩及算法研究[J].测绘通报,2003(12):16-18.
[9]徐景中,万幼川,张圣望. Li DAR地面点云的简化方法研究[J].测绘信息与工程,2008,33(1):32-34.
[10] GAO J L. 2D and 3D surface image processing algorithms and their application[D]. New Jersey:New Jersey Institute of Technology,2001:17-18.
[11]缪志修,齐华,王国昌,等.基于机载Li DAR数据构建的DEM抽稀算法研究[J].铁道勘察,2010(4):35-40.
[12]刘春,吴杭彬.基于平面不规则三角网的DEM数据压缩与质量分析[J].中国图象图形学报,2007,12(5):836-840.
[13]缪志修.基于机载Li DAR数据的DEM抽稀算法研究[D].成都:西南交通大学,2010:24-28.
[14] PAMELA S. A data density education algorithm for postprocessed air-borne Li DAR bathymetric survey data[D]. Florida:University of Florida,2000.
[15]杜浩,朱俊锋,张力,等.顾及地形特征的Li DAR点云数据抽稀算法[J].测绘科学,2016,41(9):140-146.
[16] CHOU Y H,LIU P S,DEZZANI R J.Terrain complexity and reduction of topographic data[J]. Geographical Systems,1999,1(2):179-197.
[17]胡诚.精度约束下地表Li DAR点云抽稀方法研究[D].成都:西南交通大学,2015:71-76.
[18] SONG H,FENG H Y. A point cloud simplification algorithm for mechanical part inspection[C]∥Information Technology for Balanced Manufacturing Systems.Boston,MA:Springer,2006:461-468.
[19]周启鸣,刘学军.数字地形分析[M].北京:科学出版社,2006:217-219.
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