针对北京遥测技术研究所自主研发的64通道机载单光子激光雷达，提出一种基于密度与局部统计的二维剖面点云去噪方法：在确定信号点云的高程区间后，先使用基于密度的改进空间聚类算法粗去噪，然后使用基于局部统计的统计移除离群点算法精去噪，获取信号点云。实验结果表明，本方法可适用于多种地物类型点云，高程均方根误差为0.27 m，准确率90.78%，精度高于常规点云去噪算法，满足国产机载单光子激光雷达获取高精度地表三维轮廓的技术需求。
潘超
,
李凉海
,
曹海翊
,
赵一鸣
,
袁逸飞
,
韩晓爽
. 基于密度与局部统计的单光子点云去噪方法[J]. 中国科学院大学学报, 2024
, 41(2)
: 268
-274
.
DOI: 10.7523/j.ucas.2022.058
In this paper, aiming at a 64-channel airborne single-photon LiDAR system developed by Beijing Research Institute of Telemetry, a two-dimensional profile point cloud denoising method based on density and local statistics was proposed. First, the elevation range of the point cloud was determined; then a modified DBSCAN algorithm was utilized for coarse denoising; finally, the statistical outlier removal algorithm was adapted for fine denoising and the valid signal point cloud was obtained. The experimental result shows that the method proposed in this paper can adapt to different surface types, the root mean square error of elevation is about 0.27 m, and the accuracy is 90.87%, which is better than the conventional point cloud denoising methods, and can meet the technical requirements of domestic airborne single-photon LiDAR to obtain high-precision three-dimensional surface contours.
[1] 王海伟, 丁宇星, 黄庚华, 等. 轻小型全天时远程光子计数激光雷达系统技术[J].红外与激光工程, 2019, 48(1):121-127. DOI:10.3788/IRLA201948.0106005.
[2] Brunt K M, Neumann T A, Smith B E. Assessment of ICESat-2 ice sheet surface heights, based on comparisons over the interior of the Antarctic ice sheet[J]. Geophysical Research Letters, 2019, 46(22): 13072-13078.DOI:10.1029/2019GL084886.
[3] 朱笑笑, 王成, 习晓环, 等. ICESat-2星载光子计数激光雷达数据处理与应用研究进展[J]. 红外与激光工程, 2020, 49(11): 76-85. DOI: 10.3788/IRLA20200259.
[4] 孙伟, 金建文, 李国元, 等. 激光测高卫星ICESat-2监测太湖水位精度评价[J]. 测绘科学, 2021, 46(11): 6-11. DOI: 10.16251/j.cnki.1009-2307.2021.11.002.
[5] Degnan J, Machan R, Leventhal E, et al. Cryosphere and biomass measurements using a photon-counting 3D imaging lidar[C]//CLEO:2011-Laser Applications to Photonic Applications. May 1-6，2011，Baltimore, Maryland, USA. OSA, 2011: 1-2. DOI: 10.1364/cleo_at.2011.atua3.
[6] Herzfeld U C, McDonald B W, Wallin B F, et al. Algorithm for detection of ground and canopy cover in micropulse photon-counting lidar altimeter data in preparation for the ICESat-2 mission[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(4): 2109-2125. DOI:10.1109/TGRS.2013.2258350.
[7] 夏少波, 王成, 习晓环, 等. ICESat-2机载试验点云滤波及植被高度反演[J]. 遥感学报, 2014, 18(6): 1199-1207. DOI:10.11834/ jrs.20144029.
[8] Zhang J S, Kerekes J. An adaptive density-based model for extracting surface returns from photon-counting laser altimeter data[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(4): 726-730. DOI:10.1109/LGRS.2014.2360367.
[9] Ma Y, Zhang W H, Sun J Y, et al. Photon-counting lidar: an adaptive signal detection method for different land cover types in coastal areas[J]. Remote Sensing, 2019, 11(4): 471. DOI:10.3390/rs11040471.
[10] Popescu S C, Zhou T, Nelson R, et al. Photon counting LiDAR: an adaptive ground and canopy height retrieval algorithm for ICESat-2 data[J]. Remote Sensing of Environment, 2018, 208:154-170. DOI:10.1016/j.rse.2018.02.019.
[11] 鲁冬冬, 邹进贵.三维激光点云的降噪算法对比研究[J].测绘通报，2019(S2): 102-105.DOI:10.13474/j.cnki.11-2246.2019.0599.
[12] Gan J H, Tao Y F. DBSCAN revisited: mis-claim, un-fixability, and approximation[C]//Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. May 31-June 4, 2015, Melbourne, Victoria, Australia. ACM, 2015: 519-530. DOI:10.1145/2723372.2737792.
[13] 李宗林, 罗可. DBSCAN算法中参数的自适应确定[J]. 计算机工程与应用, 2016, 52(3): 70-73, 80. DOI:10.3778/j.issn.1002-8331.1402-0278.
[14] Zhang Z Y, Ma Y, Xu N, et al. Theoretical background noise rate over water surface for a photon-counting lidar and its application in land and sea cover classification[J]. Optics Express, 2019, 27(20): A1490-A1505. DOI:10.1364/OE.27.0A1490.
[15] Rusu R B, Marton Z C, Blodow N, et al. Towards 3D point cloud based object maps for household environments[J]. Robotics and Autonomous Systems, 2008, 56(11): 927-941. DOI:10.1016/j.robot.2008.08.005.
