基于点的多尺度形态学重建滤波方法

常兵涛; 陈传法; 郭娇娇; 武慧明; 贝祎轩; 李琳叶

doi:10.11834/jrs.20210314

模型与方法 | 浏览量 : 0 下载量: 1133 CSCD: 1

R-PDF
PDF
导出
分享
收藏
专辑

基于点的多尺度形态学重建滤波方法
Point-based multi-scale morphological reconstruction filter
2022年26卷第12期页码：2582-2593
收稿：2020-08-11，

纸质出版：2022-12-07
DOI： 10.11834/jrs.20210314
稿件说明：

移动端阅览

常兵涛，陈传法，郭娇娇，武慧明，贝祎轩，李琳叶.2022.基于点的多尺度形态学重建滤波方法.遥感学报，26（12）： 2582-2593 DOI： 10.11834/jrs.20210314.

Chang B T，Chen C F，Guo J J，Wu H M，Bei Y X and Li L Y. 2022. Point-based multi-scale morphological reconstruction filter. National Remote Sensing Bulletin， 26（12）：2582-2593 DOI： 10.11834/jrs.20210314.

摘要

针对现有机载激光雷达（LiDAR）点云滤波算法难以准确分离复杂地形中地面点与地物点问题，提出了一种基于点的多尺度形态学重建滤波方法PMMF（Point-based Multi-scale Morphological reconstruction Filter）。在初始尺度层次下，PMMF通过构建一种基于点的形态学重建对原始点云滤波，即先在掩膜点云约束下借助

邻域结构元素和高程缓冲区反复膨胀标记点云，获取潜在地面点；然后通过自适应坡度方法剔除潜在地面点中的非地面点，其中，坡度阈值随地形复杂度自适应变化。在上层滤波结果基础上，PMMF通过提升种子点选择的网格尺度重复上层滤波过程，直至结果收敛。以国际摄影测量与遥感学会（ISPRS）发布的15组基准数据为研究对象，将PMMF滤波结果与近5年（2016年—2020年）提出的15种滤波算法比较表明，PMMF有8组数据滤波效果占优，15组数据平均总误差和Kappa系数分别为2.71%和91.08%。使用4种不同地形特征的高密度机载LiDAR点云数据进一步验证PMMF的滤波效果，并将计算结果与简单形态学滤波（SMRF）、布料模拟滤波（CSF）、渐进加密三角网滤波（PTD）和多分辨率层次滤波（MHF）比较。结果表明，PMMF滤波性能最优，平均总误差为3.24%，较其他4种滤波方法分别减小了12.0%、59.1%、70.1%和53.2%。

Abstract

Many airborne LiDAR point cloud filters have been proposed over the past decades. However

these existing filters are incapable of producing satisfactory results in complex landscapes

such as rugged slopes covered with low vegetation and discontinuous terrain. Thus

a point-based multi-scale morphological reconstruction filter (PMMF) is presented in this work to overcome these problems.

In contrast with the classical morphological filters

PMMF takes raw point cloud rather than rasterized grids as the basic processing element. First

the potential ground points are obtained by repeatedly dilating the marker point cloud with the k-neighbor structural element and adaptive elevation buffer under the limits of the mask point cloud. Thereafter

the non-ground points mixed in the potential ground points are eliminated by a terrain-adaptive slope filter. Based on the filtering results from the upper scale

PMMF increases the grid scale for selecting ground seeds on the next scale and repeats the filtering process as the upper level until the result converges. The three main contributions of the new algorithm include a point-based morphological method rather than a grid-based one to avoid information loss caused by point cloud rasterization

a multi-scale geodesic dilation with a slope-adaptive elevation buffer to select potential ground points and reduce the omission error on a steep terrain

and a terrain-adaptive slope filter to eliminate commission errors mixed in potential ground points.

PMMF was employed to filter the benchmark samples provided by ISPRS

and its results were compared with 15 filtering algorithms proposed in the last 5 years (2016—2020). Results illustrate that PMMF outperforms the other filtering methods on eight out of the 15 samples

and its average total error and Kappa coefficient were 2.71% and 91.08%

respectively. Moreover

PMMF was used to process four high-density airborne LiDAR point clouds with different terrain features

and the filtering results were compared with Progressive Morphological Filter (PMF)

Cloth Simulation Filter (CSF)

Progressive TIN Densification (PTD)

and multiresolution hierarchical filter (MHF). Results show that PMMF with an average total error of 3.24% has the best performance. The total error of PMMF is reduced by 12.0%

59.1%

70.1%

and 53.2% compared with those of PMF

CSF

PTD

and MHF

respectively.

A large number of experimental results show that PMMF has achieved satisfactory filtering results on various terrains

and the filtering accuracy is significantly higher than those of other conventional filtering algorithms. Experimental verification shows that the three innovations proposed in this work contribute to the higher accuracy of the new algorithm and overcome the imperfection of the existing algorithms.

关键词

Keywords

references

Axelsson P . 2000 . DEM generation from laser scanner data using adaptive TIN models . International Archives of Photogrammetry and Remote Sensing , 33 ( 4 ): 111 - 118

Bigdeli B , Amirkolaee H A and Pahlavani P . 2018 . DTM extraction under forest canopy using LiDAR data and a modified invasive weed optimization algorithm . Remote Sensing of Environment , 216 : 289 - 300 [ DOI: 10.1016/j.rse.2018.06.045 http://dx.doi.org/10.1016/j.rse.2018.06.045 ]

Chen C F , Chang B T , Li Y Y and Shi B . 2021 . Filtering airborne LiDAR point clouds based on a scale-irrelevant and terrain-adaptive approach . Measurement , 171 : 108756 [ DOI: 10.1016/j.measurement.2020.108756 http://dx.doi.org/10.1016/j.measurement.2020.108756 ]

Chen C F , Li Y Y , L W and Dai H L . 2013 . A multiresolution hierarchical classification algorithm for filtering airborne LiDAR data . ISPRS Journal of Photogrammetry and Remote Sensing , 82 : 1 - 9 [ DOI: 10.1016/j.isprsjprs.2013.05.001 http://dx.doi.org/10.1016/j.isprsjprs.2013.05.001 ]

Chen C F , Wang M Y , Chang B T and Li Y Y . 2020 . Multi-level interpolation-based filter for airborne LiDAR point clouds in forested areas . IEEE Access , 8 : 41000 - 41012 [ DOI: 10.1109/ACCESS.2020.2976848 http://dx.doi.org/10.1109/ACCESS.2020.2976848 ]

Chen Q , Gong P , Baldocchi D and Xie G X . 2007 . Filtering airborne laser scanning data with morphological methods . Photogrammetric Engineering and Remote Sensing , 73 ( 2 ): 175 - 185 [ DOI: 10.14358/PERS.73.2.175 http://dx.doi.org/10.14358/PERS.73.2.175 ]

Evans J S and Hudak A T . 2007 . A multiscale curvature algorithm for classifying discrete return LiDAR in forested environments . IEEE Transactions on Geoscience and Remote Sensing , 45 ( 4 ): 1029 - 1038 [ DOI: 10.1109/TGRS.2006.890412 http://dx.doi.org/10.1109/TGRS.2006.890412 ]

Gevaert C M , Persello C , Nex F and Vosselman G . 2018 . A deep learning approach to DTM extraction from imagery using rule-based training labels . ISPRS Journal of Photogrammetry and Remote Sensing , 142 : 106 - 123 [ DOI: 10.1016/j.isprsjprs.2018.06.001 http://dx.doi.org/10.1016/j.isprsjprs.2018.06.001 ]

Guan Y L , Chen X J and Shi G G . 2008 . A robust method for fitting a plane to point clouds . Journal of Tongji University Natural Science) , ( 7 ): 981 - 984

官云兰 , 程效军 , 施贵刚 . 2008 . 一种稳健的点云数据平面拟合方法 . 同济大学学报(自然科学版) , ( 7 ): 981 - 984 [ DOI: 10.3321/j.issn:0253-374X.2008.07.023 http://dx.doi.org/10.3321/j.issn:0253-374X.2008.07.023 ]

Hu H , Ding Y L , Zhu Q , Jiang J , Wen X H , Zhang L , Tang W , Yang J and Zhong R F . 2019 . Precision global DEM generation based on adaptive surface filter and Poisson terrain editing . Acta Geodaetica et Cartographica Sinica , 48 ( 3 ): 374 - 383

胡翰 , 丁雨淋 , 朱庆 , 蒋捷 , 文学虎 , 张力 , 唐伟 , 阳俊 , 钟若飞 . 2019 . 面向全球DEM生产的点云智能滤波与DEM泊松编辑方法 . 测绘学报 , 48 ( 3 ): 374 - 383 [ DOI: 10.11947/j.AGCS.2019.20180010 http://dx.doi.org/10.11947/j.AGCS.2019.20180010 ]

Hui Z Y , Hu Y J , Yevenyo Y Z and Yu X Y . 2016 . An improved morphological algorithm for filtering airborne LiDAR point cloud based on multi-level kriging interpolation . Remote Sensing , 8 ( 1 ): 35 [ DOI: 10.3390/rs8010035 http://dx.doi.org/10.3390/rs8010035 ]

Jahromi A B , Zoej M J V , Mohammadzadeh A and Sadeghian S . 2011 . A novel filtering algorithm for bare-earth extraction from airborne laser scanning data using an artificial neural network . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 4 ( 4 ): 836 - 843 [ DOI: 10.1109/JSTARS.2011.2132793 http://dx.doi.org/10.1109/JSTARS.2011.2132793 ]

Jesús Balado , Peter van Oosterom , Lucía Díaz-Vilariño and Martijn Meijers . 2020 . Mathematical morphology directly applied to point cloud data . ISPRS Journal of Photogrammetry and Remote Sensing , 168 : 208 - 220 [ DOI: 10.1016/j.isprsjprs.2020.08.011 http://dx.doi.org/10.1016/j.isprsjprs.2020.08.011 ]

Kilian J , Haala N and Englich M . 1996 . Capture and evaluation of airborne laser scanner data . International Archives of Photogrammetry and Remote Sensing , 31 ( B3 ): 383 - 388 [ DOI: 10.1.1.17.1633 http://dx.doi.org/10.1.1.17.1633 ]

Kraus K and Pfeifer N . 1998 . Determination of terrain models in wooded areas with airborne laser scanner data . ISPRS Journal of Photogrammetry and Remote Sensing , 53 ( 4 ): 193 - 203 [ DOI: 10.1016/S0924-2716(98)00009-4 http://dx.doi.org/10.1016/S0924-2716(98)00009-4 ]

Li Y , Yong B , Van Oosterom P , Lemmens M , Wu H Y , Ren L L , Zheng M X and Zhou J J . 2017 . Airborne LiDAR data filtering based on geodesic transformations of mathematical morphology . Remote Sensing , 9 ( 11 ): 1104 [ DOI: 10.3390/rs9111104 http://dx.doi.org/10.3390/rs9111104 ]

Li Y , Yong B , Wu H Y , An R and Xu H W . 2014 . An improved top-hat filter with sloped brim for extracting ground points from airborne lidar point clouds . Remote Sensing , 6 ( 12 ): 12885 - 12908 [ DOI: 10.3390/rs61212885 http://dx.doi.org/10.3390/rs61212885 ]

Lin X G and Zhang J X . 2014 . Segmentation-based filtering of airborne LiDAR point clouds by progressive densification of terrain segments . Remote Sensing , 6 ( 2 ): 1294 - 1326 [ DOI: 10.3390/rs6021294 http://dx.doi.org/10.3390/rs6021294 ]

Liu H , Zhang Z N and Cao L . 2018 . Estimating forest stand characteristics in a coastal plain forest plantation based on vertical structure profile parameters derived from ALS data . Journal of Remote Sensing , 22 ( 5 ): 872 - 888

刘浩 , 张峥男 , 曹林 . 2018 . 机载激光雷达森林垂直结构剖面参数的沿海平原人工林林分特征反演 . 遥感学报 , 22 ( 5 ): 872 - 888 [ DOI: 10.11834/jrs.20187465 http://dx.doi.org/10.11834/jrs.20187465 ]

Liu X Y . 2008 . Airborne LiDAR for DEM generation: some critical issues . Progress in Physical Geography : Earth and Environment , 32 ( 1 ): 31 - 49 [ DOI: 10.1177/0309133308089496 http://dx.doi.org/10.1177/0309133308089496 ]

Lu W L , Murphy K P , Little J J , Sheffer A and Fu H B . 2009 . A hybrid conditional random field for estimating the underlying ground surface from airborne LiDAR data . IEEE Transactions on Geoscience and Remote Sensing , 47 ( 8 ): 2913 - 2922 [ DOI: 10.1109/TGRS.2009.2017738 http://dx.doi.org/10.1109/TGRS.2009.2017738 ]

Luo Y M , Ma H C and Zhou L G . 2017 . DEM retrieval from airborne LiDAR point clouds in mountain areas via deep neural networks . IEEE Geoscience and Remote Sensing Letters , 14 ( 10 ): 1770 - 1774 [ DOI: 10.1109/LGRS.2017.2734920 http://dx.doi.org/10.1109/LGRS.2017.2734920 ]

Ma H C , Yao C J and Zhang S D . 2008 . Some technical issues of airborne LiDAR system applied to wenchuan earthquake relief works . Journal of Remote Sensing , 12 ( 6 ): 925 - 932

马洪超 , 姚春静 , 张生德 . 2008 . 机载激光雷达在汶川地震应急响应中的若干关键问题探讨 . 遥感学报 , 12 ( 6 ): 925 - 932 [ DOI: 10.11834/jrs.200806124 http://dx.doi.org/10.11834/jrs.200806124 ]

Meng X L , Currit N and Zhao K G . 2010 . Ground filtering algorithms for airborne LiDAR data: a review of critical issues . Remote Sensing , 2 ( 3 ): 833 - 860 [ DOI: 10.3390/rs2030833 http://dx.doi.org/10.3390/rs2030833 ]

Meng X S , Lin Y , Yan L , Gao X L , Yao Y J , Wang C and Luo S Z . 2019 . Airborne LiDAR point cloud filtering by a multilevel adaptive filter based on morphological reconstruction and thin plate spline interpolation . Electronics , 8 ( 10 ): 1153 [ DOI: 10.3390/electronics8101153 http://dx.doi.org/10.3390/electronics8101153 ]

Mongus D , Lukač N and Žalik B . 2014 . Ground and building extraction from LiDAR data based on differential morphological profiles and locally fitted surfaces . ISPRS Journal of Photogrammetry and Remote Sensing , 93 : 145 - 156 [ DOI: 10.1016/j.isprsjprs.2013.12.002 http://dx.doi.org/10.1016/j.isprsjprs.2013.12.002 ]

Pingel T J , Clarke K C and Mcbride W A . 2013 . An improved simple morphological filter for the terrain classification of airborne LIDAR data . ISPRS Journal of Photogrammetry and Remote Sensing , 77 : 21 - 30 [ DOI: 10.1016/j.isprsjprs.2012.12.002 http://dx.doi.org/10.1016/j.isprsjprs.2012.12.002 ]

Shao Y C and Chen L C . 2008 . Automated searching of ground points from airborne lidar data using a climbing and sliding method . Photogrammetric Engineering and Remote Sensing , 74 ( 5 ): 625 - 635 [ DOI: 10.14358/PERS.74.5.625 http://dx.doi.org/10.14358/PERS.74.5.625 ]

Sithole G and Vosselman G . 2001 . Filtering of laser altimetry data using a slope adaptive filter . International Archives of Photogrammetry and Remote Sensing , 34 ( 3 /W 4 ): 203 - 210

Soille P . 2003 . Morphological Image Analysis: Principles and Applications . 2nd ed . New York : Springer

Su W , Sun Z P , Zhao D L , Sun C L , Zhang C and Yang J Y . 2009 . Hierarchical moving curved fitting filtering method based on LIDAR data . Journal of Remote Sensing , 13 ( 5 ): 827 - 839

苏伟 , 孙中平 , 赵冬玲 , 孙崇利 , 张超 , 杨建宇 . 2009 . 多级移动曲面拟合LIDAR数据滤波算法 . 遥感学报 , 13 ( 5 ): 827 - 839 [ DOI: 10.11834/jrs.20090506 http://dx.doi.org/10.11834/jrs.20090506 ]

Susaki J . 2012 . Adaptive slope filtering of airborne LiDAR data in urban areas for digital terrain model (DTM) generation . Remote Sensing , 4 ( 6 ): 1804 - 1819 [ DOI: 10.3390/rs4061804 http://dx.doi.org/10.3390/rs4061804 ]

Tóvári D and Pfeifer N . 2005 . Segmentation based robust interpolation—A new approach to laser data filtering // Proceedings of the Laser Scanning 2005 . Enschede: 79 - 84

Vosselman G . 2000 . Slope based filtering of laser altimetry data . International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences , 33 : 935 - 942 [ DOI: 10.1016/S0924-2716(98)00009-4 http://dx.doi.org/10.1016/S0924-2716(98)00009-4 ]

Wang C K and Tseng Y H . 2010 . DEM gemeration from airborne lidar data by an adaptive dualdirectional slope filter . International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives , 38 : 628 - 632

Yang B S , Huang R G , Dong Z , Zang Y F and Li J P . 2016 . Two-step adaptive extraction method for ground points and breaklines from lidar point clouds . ISPRS Journal of Photogrammetry and Remote Sensing , 119 : 373 - 389 [ DOI: 10.1016/j.isprsjprs.2016.07.002 http://dx.doi.org/10.1016/j.isprsjprs.2016.07.002 ]

Zhan Z Q , Hu M Q and Man Y Y . 2020 . Multi-scale region growing point cloud filtering method based on surface fitting . Acta Geodaetica et Cartographica Sinica , 49 ( 6 ): 757 - 766

詹总谦 , 胡孟琦 , 满益云 . 2020 . 多尺度区域生长点云滤波地表拟合法 . 测绘学报 , 49 ( 6 ): 757 - 766 [ DOI: 10.11947/j.AGCS.2020.20190142 http://dx.doi.org/10.11947/j.AGCS.2020.20190142 ]

Zhang K Q , Chen S C , Whitman D , Shyu M L , Yan J H and Zhang C C . 2003 . A progressive morphological filter for removing nonground measurements from airborne LIDAR data . IEEE Transactions on Geoscience and Remote Sensing , 41 ( 4 ): 872 - 882 [ DOI: 10.1109/TGRS.2003.810682 http://dx.doi.org/10.1109/TGRS.2003.810682 ]

Zhang W M , Qi J B , Wan P , Wang H T , Xie D H , Wang X Y and Yan G J . 2016 . An easy-to-use airborne LiDAR data filtering method based on cloth simulation . Remote Sensing , 8 ( 6 ): 501 [ DOI: 10.3390/rs8060501 http://dx.doi.org/10.3390/rs8060501 ]

文章被引用时，请邮件提醒。

提交

顾及地形特征和边界防收缩的机载LiDAR地面点云聚类简化方法

协同多源遥感数据的北亚热带森林蓄积量贝叶斯分层估测