基于改进窗口尺寸的LiDAR点云数据滤波
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摘要
激光LiDAR点云数据的滤波处理是激光LiDAR数据处理的基础和关键技术,数学形态学的滤波算法在许多领域应用广泛。鉴于传统数学形态学滤波算法中窗口尺寸确定的问题,提出了一种改进的渐进式数学形态学滤波方法。通过改进窗口尺寸给定的数学公式,不断地迭代窗口尺寸大小以及相应的高程差阈值。依据数学形态学滤波的基本原理,证明了改进窗口尺寸这公式的可行性。利用LiDAR点云数据,对所提得方法进行了实验论证。实验结果显示,改进后的方法可以有效地滤除大多数的非地面点,并且同时使得Ⅰ类误差率平均下降了1.47%,总误差率平均下降了0.72%。
Filtering of laser LiDAR point cloud data is the foundation and key technology of laser LiDAR data processing.The mathematical morphology filtering algorithm has wide application in many fields.In view of the problem of window size determination in classical mathematical morphology filtering algorithm,we improve the mathematical formula given by window size.The size of the window and the corresponding elevation difference threshold are continuously iterated by improving the mathematical formula given by the window size.According to the basic principle of mathematical morphology filtering,the feasibility of improving the window size is proved.Using the LiDAR point cloud data,the proposed method is experimentally demonstrated.The experimental results show that the improved method can effectively filter out most of the non-ground points,at the same time,the average error rate of class I dropped by 1.47% and the total error rate decreased by 0.72% on average.
Filtering of laser LiDAR point cloud data is the foundation and key technology of laser LiDAR data processing.The mathematical morphology filtering algorithm has wide application in many fields.In view of the problem of window size determination in classical mathematical morphology filtering algorithm,we improve the mathematical formula given by window size.The size of the window and the corresponding elevation difference threshold are continuously iterated by improving the mathematical formula given by the window size.According to the basic principle of mathematical morphology filtering,the feasibility of improving the window size is proved.Using the LiDAR point cloud data,the proposed method is experimentally demonstrated.The experimental results show that the improved method can effectively filter out most of the non-ground points,at the same time,the average error rate of class I dropped by 1.47% and the total error rate decreased by 0.72% on average.
引文
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[11]罗伊萍,姜挺,王鑫,等.基于数学形态学的LiDAR数据滤波新方法[J].测绘通报,2011(3):15-19.
[12]穆超,余洁,许磊,等.基于高分辨率遥感影像的DSM建筑物点的提取研究[J].武汉大学学报(信息科学版),2009(4):414-417.
[13]沈晶,刘纪平,林祥国.用形态学重建方法进行机载LiDAR数据滤波[J].武汉大学学报(信息科学版),2011(2):167-170,175.
[14]黄先锋,李卉,王潇,等.机载LiDAR数据滤波方法评述[J].测绘学报,2009(5):466-469.
[15]张熠斌,隋立春,曲佳,等.基于数学形态学算法的机载LiDAR点云数据快速滤波[J].测绘通报,2009(5):16-18.
[2]赵明波,何峻,田军生,等.基于改进的渐进多尺度数学形态学的激光雷达数据滤波方法[J].光学学报,2013(3):292-301.
[3]李峰,崔希民,袁德宝,等.改进坡度的LiDAR点云形态学滤波算法[J].大地测量与地球动力学,2012(5):128-132.
[4]董保根,秦志远,朱传新,等.关于机载LiDAR点云数据形态学滤波的几点思考[J].测绘科学,2013(4):19-21.
[5]赵明波,何峻,付强.全波形激光雷达回波信号建模仿真与分析[J].光学学报,2012(6):246-259.
[6]Axelsson P.DEM generation from laser scanner data using adaptive TIN models[J].International Archives of Photogrammetry and Remote Sensing,2000,33(B4/1):110-117.
[7]Kraus K,Pfeifer N.Determination of terrain models in wooded areas with airborne laser scanner data[J].ISPRS Journal of Photogrammetry and Remote Sensing,1998,53(4):193-203.
[8]Pfeifer N,Stadler P.Derivation of digital terrain models in the SCOP environment[C]//OEEPE workshop on airborne laser scanning and interferometric SAR for digital elevation models.Stockholm,2001.
[9]孙美玲,李永树,陈强,等.基于开重建的LiDAR数据形态学滤波方法[J].大地测量与地球动力学,2014(2):90-94.
[10]罗伊萍,姜挺,龚志辉,等.基于自适应和多尺度数学形态学的点云数据滤波方法[J].测绘科学技术学报,2009(6):426-429.
[11]罗伊萍,姜挺,王鑫,等.基于数学形态学的LiDAR数据滤波新方法[J].测绘通报,2011(3):15-19.
[12]穆超,余洁,许磊,等.基于高分辨率遥感影像的DSM建筑物点的提取研究[J].武汉大学学报(信息科学版),2009(4):414-417.
[13]沈晶,刘纪平,林祥国.用形态学重建方法进行机载LiDAR数据滤波[J].武汉大学学报(信息科学版),2011(2):167-170,175.
[14]黄先锋,李卉,王潇,等.机载LiDAR数据滤波方法评述[J].测绘学报,2009(5):466-469.
[15]张熠斌,隋立春,曲佳,等.基于数学形态学算法的机载LiDAR点云数据快速滤波[J].测绘通报,2009(5):16-18.