首页 > 论文 > 激光与光电子学进展 > 55卷 > 4期(pp:42801--1)

基于多光谱数据指导的偏度平衡点云滤波

Point Cloud Filter of Skewness Balance Based on the Guidance of Multispectral Data

  • 摘要
  • 论文信息
  • 参考文献
  • 被引情况
  • PDF全文
分享:

摘要

针对现有激光雷达(LiDAR)点云滤波方法无法有效排除数字表面模型(DSM)中数据空洞干扰的问题,提出了基于多光谱数据指导的偏度平衡点云滤波方法。该方法将多光谱数据引入点云滤波并将其作为引导图像,实现了与噪声点光谱相似点的快速去噪。实验结果表明,该方法有效排除了数据空洞对点云滤波造成的干扰,所得到的滤波误差与原有偏度平衡点云滤波方法相比减少了0.4%~0.8%;与目前流行的基于支持向量机(SVM)的滤波算法相比,该方法的误差减少了0.1%~0.4%。

Abstract

Aiming at the problem that the existing light detection and ranging (LiDAR) point cloud filtering method cannot effectively exclude the data hole interference in the digital surface model (DSM), a skewness balance point cloud filtering method based on multispectral data guidance is proposed. This method introduces the multispectral data into the point cloud filter as the guiding image to realize the fast denoising with the spectral similarity of the noise points. The experimental results show that this method can effectively eliminate the interference caused by the data hole to the point cloud filtering, and the obtained filtering error is reduced by 0.4%-0.8% compared with the original skewness point cloud filtering method. Compared with the popular filter algorithm based on support vector machines (SVM), the error of this method is reduced by 0.1%-0.4%.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TN911.73

DOI:10.3788/lop55.042801

所属栏目:遥感与传感器

基金项目:国家自然科学基金(61672472)、中北大学科学研究基金(XJJ2016024)、中北大学电子测试技术重点实验室开放基金(ZDSYSJ2015005)

收稿日期:2017-09-15

修改稿日期:2017-10-08

网络出版日期:--

作者单位    点击查看

韩晓峰:中北大学信息与通信工程学院, 山西 太原 030051
杨风暴:中北大学信息与通信工程学院, 山西 太原 030051
卫红:英国雷丁大学系统工程学院, 伯克郡 雷丁 RG6 6AU
李大威:中北大学信息与通信工程学院, 山西 太原 030051
刘丹:中北大学信息与通信工程学院, 山西 太原 030051

联系人作者:杨风暴(18903438847@163.com)

备注:韩晓峰(1989-),男,硕士研究生,主要从事LiDAR数据处理与应用方面的研究 。 E-mail: 511310204@qq.com

【1】Wang L J, Huang R G, Wan J H, et al. A skewness balancing method for LiDAR point cloud filtering [J]. Hydrographic Surveying and Charting, 2013, 33 (5): 42-45.
王力军, 黄荣刚, 万剑华, 等. 一种基于偏度平衡的LiDAR点云滤波方法[J]. 海洋测绘, 2013, 33(5):42-45.

【2】Hu Y J, Cheng P G, Chen X Y, et al. The analysis and comparison of airborne LiDAR point cloud filter algorithms [J]. Journal of Geomatics Science and Technology, 2015(1): 72-77.
胡永杰, 程朋根, 陈晓勇, 等. 机载激光雷达点云滤波算法分析与比较[J]. 测绘科学技术学报, 2015(1): 72-77.

【3】Bartels M, Wei H. Segmentation of LIDAR data using measures of distribution[J]. International Archives of Photogrammetry, 2012: 289-304.

【4】Weidner U, Frstner W. Towards automatic building extraction from high-resolution digital elevation models[J]. ISPRS Journal of Photogrammetry & Remote Sensing, 1995, 50(4): 38-49.

【5】Cobby D M, Mason D C, Horritt M S, et al. Two-dimensional hydraulic flood modelling using a finite-element mesh decomposed according to vegetation and topographic features derived from airborne scanning laser altimetry[J]. Hydrological Processes, 2003, 17(10): 1979-2000.

【6】Bartels M, Wei H, Mason D C. Wavelet packets and co-occurrence matrices for texture-based image segmentation[C]. IEEE Conference on Advanced Video and Signal Based Surveillance, 2005: 428-433.

【7】Sithole G, Vosselman G. Automatic structure detection in a point-cloud of an urban landscape[C]. IEEE Workshop on Remote Sensing and Data Fusion over Urban Areas, 2003: 67-71.

【8】Lin J Y, Zou S L, Hu Y J. LiDAR data filtering method based on progressive morphological open operation and skewness balancing method [J]. Journal of Anhui Agricultural Sciences, 2015(16): 351-354.
林金彦, 邹时林, 胡永杰. 基于渐进形态学开运算和偏度平衡法的LiDAR数据滤波方法[J]. 安徽农业科学, 2015(16): 351-354.

【9】Zhang Y J, Wu L, Lin L W, et al. Automatic extraction of water bodies based on LiDAR data and aerial images [J]. Geomatics and Information Science of Wuhan University, 2010, 35 (8): 936-940.
张永军, 吴磊, 林立文, 等. 基于LiDAR数据和航空影像的水体自动提取[J]. 武汉大学学报(信息科学版), 2010, 35(8): 936-940.

【10】Duda R, Hart P, Stork D. Pattern classification[M]. New York: John Wiley & Sons, 2000.

【11】David F N. A statistical primer[J]. A Statistical Primer, 1953, 24(5): 338-343.

【12】Longhi P, Mussini T, Orsenigo R, et al. Redetermination of the standard potential of the mercuric oxide electrode at temperatures between 283 and 363 K and the solubility product constant of mercuric hydroxide[J]. Journal of Applied Electrochemistry, 1987, 17(3): 505-514.

【13】Miao Q G, Guo X, Song J F, et al. LiDAR point cloud data with morphological filter algorithm based on region prediction [J]. Laser & Optoelectronics Progress, 2015, 52 (1): 011003.
苗启广, 郭雪, 宋建锋, 等. 基于区域预测的LiDAR点云数据形态学滤波算法[J]. 激光与光电子学进展, 2015, 52(1): 011003.

【14】Duan Y H, Zhang A W, Liu Z, et al. A Gaussian inflexion points matching method for Gaussian decomposition of airborne LiDAR waveform data [J]. Laser & Optoelectronics Progress, 2014, 51 (10): 102801.
段乙好, 张爱武, 刘诏, 等. 一种用于机载LiDAR波形数据高斯分解的高斯拐点匹配法[J]. 激光与光电子学进展, 2014, 51(10): 102801.

【15】Yue G M, Wu Y H, Hu S X, et al. A multi-wavelength simultaneous output laser system for lidar[J]. Chinese Journal of Lasers, 2002, 29(s1): 215-217.
岳古明, 吴永华, 胡顺星, 等. 用于激光雷达的多波长同时输出激光系统[J]. 中国激光, 2002, 29(s1): 215-217.

引用该论文

Han Xiaofeng,Yang Fengbao,Wei Hong,Li Dawei,Liu Dan. Point Cloud Filter of Skewness Balance Based on the Guidance of Multispectral Data[J]. Laser & Optoelectronics Progress, 2018, 55(4): 042801

韩晓峰,杨风暴,卫红,李大威,刘丹. 基于多光谱数据指导的偏度平衡点云滤波[J]. 激光与光电子学进展, 2018, 55(4): 042801

您的浏览器不支持PDF插件,请使用最新的(Chrome/Fire Fox等)浏览器.或者您还可以点击此处下载该论文PDF