强度体元基元下的机载LiDAR 3D滤波

doi:10.12082/dqxxkx.2019.190152

摘要/Abstract

摘要：

针对现有基于二值体元基元的机载LiDAR三维（3 Dimensional, 3D）滤波算法仅利用了数据的高程特征、无法区分相连的地面和非地面目标的问题,提出了一种基于强度体元基元的机载LiDAR 3D滤波算法。首先,基于计算几何理论,将机载LiDAR数据规则化为强度（体元内激光点的量化平均反射强度值）体元结构。然后,基于3D连通区域构建理论,选取局部高程最低的非0值体元为地面种子进而搜寻并标记与地面种子,空间连通、反射强度及坡度值均接近的连通区域内体元为地面体元。算法综合利用LiDAR数据的高程、反射强度及坡度特征,支持相连但强度不同的地面和非地面目标的区分,为相连的地面和非地面目标的精确区分提供更有效的信息。算法有助于提高滤波精度,并扩展基于体元基元的3D滤波算法适用于更复杂的场景。实验基于ISPRS提供的专门用于滤波算法测试的LiDAR点云数据测试了“空间邻域尺度”参数的敏感性及提出算法的精度。定量评价的结果表明：51邻域为最佳邻域尺度;提出算法的平均Kappa系数在相对平坦、陡坡及不连续地形分别为0.9380、0.7749和0.6866;从总误差测度来看,提出算法对比经典的Axelsson算法改进了15个样本中的7个样本精度,且其对比其他二值体元基元下的滤波算法平均总误差最低。

关键词: 激光雷达, 滤波, 体元, 强度, 连通区域, 空间邻域尺度

Abstract:

The existing binary voxel primitive based 3-Dimensional (3D) filtering algorithms for airborne Light Detection And Ranging (LiDAR) data, which use only elevation features, cannot distinguish between connected ground and non-ground objects. As a result, an airborne LiDAR 3D filtering algorithm based on intensity voxel primitive was proposed in the present study. First, airborne LiDAR data were regularized into intensity voxel structure based on computational geometry theory, in which intensity value of the voxel corresponds to the quantized intensity of the LiDAR point(s) within the voxel. Second, based on the theory of 3D connected region construction, the non-zero voxels with the lowest local elevation was selected as ground seeds, and then ground seeds and their connected regions where the voxels are 3D connected and have similar grayscales and slope with seeds were labelled as ground voxels. The proposed algorithm makes comprehensive use of the features of elevation, reflection intensity, and slope, supports 3D filtering in areas where the ground are adjacent to non-ground objects but with different intensities, and provides more effective information for the accurate distinction between the connected ground and non-ground objects. The proposed algorithm is helpful to improve the filtering accuracy and extend voxel primitive based 3D filtering algorithm for more complex scenes. The International Society for Photogrammetry and Remote Sensing (ISPRS) benchmark dataset, which contains a variety of features that is expected to be difficult for automatic filtering, were used to analyze the sensitivity of “spatial adjacency size” parameter in the proposed algorithm and to assess the accuracy of the proposed algorithm quantitatively. Results show: （1） The 51-adjacency was the optimal spatial adjacent size. （2） The average Kappa coefficient of the proposed algorithm was 0.9380, 0.7749, and 0.6866 in relatively flat, steep slope, and discontinuous terrain areas, respectively. （3） In terms of total error, the proposed algorithm improved the accuracy of 7 out of 15 samples and had a higher accuracy than all other binary voxel primitive based 3D filtering algorith-ms on average.

Key words: LiDAR, filtering, voxel, intensity, connected region, spatial adjacency size

王丽英, 王圣, 李玉. 强度体元基元下的机载LiDAR 3D滤波[J]. 地球信息科学学报, 2019, 21(12): 1945-1954.DOI:10.12082/dqxxkx.2019.190152

WANG Liying, WANG Sheng, LI Yu. Airborne LiDAR 3D Filtering based on Intensity Voxel Primitive[J]. Journal of Geo-information Science, 2019, 21(12): 1945-1954.DOI:10.12082/dqxxkx.2019.190152

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参考文献 30

[1]	张继贤, 林祥国, 梁欣廉 . 点云信息提取研究进展和展望. 测绘学报, 2017,46(10):1460-1469.
	[ Zhang J X, Lin X G, Liang X L . Advances and prospects of information extraction from point clouds[J]. Acta Geodaetica et Cartographica Sinica, 2017,46(10):1460-1469. ]
[2]	Axelsson P . DEM generation from laser scanner data using adaptive TIN models[J]. International Archives of Photogrammetry and Remote Sensing, 2000,33(B4):111-118.
[3]	Zhang W, Qi J, Wan P , et al. An easy-to-use airborne LiDAR data filtering method based on cloth simulation[J]. Remote Sensing, 2016,8(6):501. doi: 10.3390/rs8060501
[4]	郭波, 黄先锋, 张帆 , 等. 顾及空间上下文关系的Joint Boost点云分类及特征降维[J]. 测绘学报, 2013,42(5):715-721.
	[ Guo B, Huang X F, Zhang F , et al. Points cloud classification using JointBoost combined with contextual information for feature reduction[J]. Acta Geodaetica et Cartographica Sinica, 2013,42(5):715-721. ]
[5]	Liu X Q, Chen Y M, Cheng L , et al. Airborne laser scanning point clouds filtering method based on the construction of virtual ground seed points[J]. Journal of Applied Remote Sensing, 2017,11(1):016032. doi: 10.1117/1.JRS.11.016032
[6]	Morsy S, Shaker A, El-rabbany A . Multispectral LiDAR data for land cover classification of urban areas[J]. Sensors, 2017,17(5):958. doi: 10.1371/journal.pone.0206185 pmid: 30356306
[7]	Nie S, Wang C, Dong P L , et al. A revised progressive TIN densification for filtering airborne LiDAR data[J]. Measurement, 2017,104:70-77. doi: 10.1016/j.measurement.2017.03.007
[8]	黄作维, 刘峰, 胡光伟 . 基于多尺度虚拟格网的LiDAR点云数据滤波改进方法[J]. 光学学报, 2017,37(8):346-355.
	[ Huang Z W, Liu F Z, Hu G W . Improved method for LiDAR point cloud data filtering based on hierarchical pseudo-grid[J]. Acta Optica Sinica, 2017,37(8):346-355. ]
[9]	Chen C, Li Y, Zhao N , et al. A fast and robust interpolation filter for airborne lidar point clouds[J]. Plos One, 2017,12(5):1-20. doi: 10.1371/journal.pone.0176954 pmid: 28467478
[10]	朱笑笑, 王成, 习晓环 , 等. 多级移动曲面拟合的自适应阈值点云滤波方法[J]. 测绘学报, 2018,47(2):153-160.
	[ Zhu X X, Wang C, Xi X H , et al. Hierarchical threshold adaptive for point cloud filter algorithm of moving surface fitting[J]. Acta Geodaetica et Cartographica Sinica, 2018,47(2):153-160. ]
[11]	Wang C K, Seng Y H, Chu H J . Airborne dual-wavelength LiDAR data for classifying landcover[J]. Remote Sensing, 2014,6(1):700-715. doi: 10.3390/rs6010700
[12]	Axelsson A, Linberg E, Olsson H . Exploring multispectral ALS data for tree species classification[J]. Remote Sensing, 2018,10(2):183. doi: 10.3390/rs10020183
[13]	Hui Z, Hu Y, Yevenyo Y , et al. An improved morphological algorithm for filtering airborne LiDAR point cloud based on multi-level kriging interpolation[J]. Remote Sensing, 2016,8(5):1-16. doi: 10.3390/rs8010001
[14]	Pingel T J, Clarke K C, Mcbride W A . An improved simple morphological filter for the terrain classification of airborne LIDAR data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013,77(1):21-30. doi: 10.1016/j.isprsjprs.2012.12.002
[15]	Chen Q, Gong P, Baldocchi D , et al. Filtering airborne laser scanning data with morphological methods[J]. Photogrammetric Engineering and Remote Sensing, 2007,73(2):175-185. doi: 10.14358/PERS.73.2.175
[16]	隋立春, 张熠斌, 柳艳 , 等. 基于改进的数学形态学算法的LiDAR点云数据滤波[J]. 测绘学报, 2012,39(4):390-396.
	[ Sui L C, Zhang Y B, Liu Y , et al. Filtering of airborne LiDAR point cloud data based on the adaptive mathematical morphology[J]. Acta Geodaetica et Cartographica Sinica, 2012,39(4):390-396. ]
[17]	潘锁艳, 管海燕 . 机载多光谱LiDAR 数据的地物分类力法[J]. 测绘学报, 2018,47(2):198-207.
	[ Pan S Y, Guan H Y . Object classification using airborne multispectral LiDAR data[J]. Acta Geodaetica et Cartographica Sinica, 2018,47(2):198-207. ]
[18]	Huo L, Silva C A, Klauberg C , et al. Supervised spatial classification of multispectral LiDAR data in urban areas[J]. Plos One, 2018,13(10):e0206185. doi: 10.1371/journal.pone.0206185 pmid: 30356306
[19]	Zou X, Zhao G, Li J, , et al. 3D land cover classification based on multispectral lidar point clouds[J]. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences 2016, XLI-B1:741-747.
[20]	Matikainen L, Karila K, Hyyppa J , et al. Object-based analysis of multispectral airborne laser scanner data for land cover classification and map updating[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017,128:298-313. doi: 10.1016/j.isprsjprs.2017.04.005
[21]	Hu J, Yang L, Shen J , et al. Filtering of LIDAR based on segmentation[J]. Geomatics and Information Science of Wuhan University, 2012,37(3):318-321.
[22]	Filin S . Surface clustering from airborne laser scanning data[J]. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, 2002,34(3/A):119-124.
[23]	Yan M, Blaschke T, Liu Y , et al. An object-based analysis filtering algorithm for airborne laser scanning[J]. International Journal of Remote Sensing, 2012,33(22):7099-7116. doi: 10.1080/01431161.2012.699694
[24]	Lin X, Zhang J . Segmentation-based filtering of airborne LIDAR point clouds by progressive densification of terrain segments[J]. Remote Sensing, 2014,6(2):1294-1326. doi: 10.3390/rs6021294
[25]	Shan J, Sampath A . Urban DEM generation from raw lidar data: a labeling algorithm and its performance[J]. Photogrammetric Engineering and Remote Sensing, 2005,71(2):217-226. doi: 10.14358/PERS.71.2.217
[26]	Hu X, Li X, Zhang Y . Fast filtering of LiDAR point cloud in urban areas based on scan line segmentation and GPU acceleration[J]. IEEE Geoscience and Remote Sensing Letters, 2013,10(2):308-312. doi: 10.1109/LGRS.2012.2205130
[27]	Sithole G, Vosselman G. Automatic structure detection in a point-cloud of an urban landscape[C]//Proceedings of the 2^nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, Berlin: IEEE, 2003: 67-71.
[28]	Wang L, Xu Y, Li Y . Aerial LIDAR point cloud voxelization with its 3D ground filtering application[J]. Photogrammetric Engineering and Remote Sensing, 2017,83(2):95-107. doi: 10.14358/PERS.83.2.95
[29]	王丽英, 徐艳, 李玉 . 机载LiDAR点云体元化及其在3D滤波中的应用[J]. 仪器仪表学报, 2018,39(7):173-182.
	[ Wang L Y, Xu Y, Li Y . Aerial LIDAR point cloud voxelization and its 3D filtering application[J]. Chinese Journal of Scientific Instrument, 2017,83(2):95-107. ]
[30]	赵泉华, 石雪, 王玉 , 等. 可变类空间约束高斯混合模型遥感图像分割[J]. 通信学报, 2017,38(2):34-43.
	[ Zhao Q H, Shi X, Wang Y , et al. Remote sensing image segmentation based on spatially constrained gaussian mixture model with unknown class number[J]. Journal on Communications, 2017,38(2):34-43. ]

样本	特征	点数/个	密度/点/m^-2	地形特征
Samp11	城区	38 010	1.066	陡坡上混合有植被和建筑物
Samp12	城区	52 119	1.036	小物体（车辆等）、混合植被和建筑物
Samp21	城区	12 960	1.096	狭窄的桥梁、植被
Samp22	城区	32 706	1.039	桥梁（西南方向）、通道（东北方向）
Samp23	城区	25 095	1.198	大的不规则形状的建筑物
Samp24	城区	7492	1.172	斜坡
Samp31	城区	28 862	0.976	复杂屋顶、高低混合地物
Samp41	城区	11 231	1.553	大的数据空洞、不规则建筑物
Samp42	城区	42 470	1.084	铁路、高频率的地形起伏
Samp51	山区	17 845	5.509	斜坡、密集植被
Samp52	山区	22 474	5.984	断层、矮小植被
Samp53	山区	34 378	5.689	间断的陡峭地形
Samp54	山区	8608	5.758	地形表面有密集覆盖物
Samp61	山区	35 060	6.382	不连续的陡坡、山脊、沟渠
Samp71	山区	15 645	5.403	间断地形、地下通道

样本	Kappa系数
样本	6邻域	18邻域	26邻域	56邻域	64邻域	51邻域
城区 Samp11	0.4271	0.5110	0.5303	0.6495	0.6551	0.6729
Samp12	0.8779	0.9042	0.9059	0.9232	0.9228	0.9232
Samp21	0.9303	0.9494	0.9485	0.9473	0.9462	0.9538
Samp22	0.7958	0.8375	0.8624	0.9062	0.9026	0.9064
Samp23	0.8499	0.8855	0.8880	0.9144	0.9148	0.9188
Samp24	0.7274	0.8006	0.8143	0.8643	0.8612	0.8892
Samp31	0.9544	0.9624	0.9646	0.9701	0.9694	0.9718
Samp41	0.9040	0.9386	0.9398	0.9389	0.9389	0.9409
Samp42	0.9571	0.9771	0.9775	0.9790	0.9789	0.9806
山区 Samp51	0.7763	0.7937	0.7911	0.7670	0.7647	0.7751
Samp52	0.2107	0.4741	0.6038	0.7606	0.7607	0.7623
Samp53	0.3138	0.4430	0.4977	0.5049	0.5000	0.5162
Samp54	0.8626	0.9020	0.9062	0.9065	0.9058	0.9086
Samp61	0.5342	0.6877	0.7337	0.7564	0.7485	0.7648
Samp71	0.6652	0.7263	0.7417	0.6917	0.6942	0.7787
平均	0.7191	0.7862	0.8070	0.8320	0.8309	0.8442

点云数据	点数/个	场景空间范围/m	体元尺寸/m	体元模型尺寸/个	非0值体元数/个	地面体元数/个	总误差/%
原始	25 094	146×205.9×86.0	1.1×1.1×0.5	135×189×174	18 973	10 319	0.0505
剔除异常数据	25 077	146×105.9×43.5	1.1×1.1×0.4	135×189×110	19 344	10 351	0.0404

样本	I_e	II_e	T_e	Kappa系数
Samp11	0.1546	0.1693	0.1609	0.6729
Samp12	0.0410	0.0356	0.0384	0.9232
Samp21	0.0107	0.0344	0.0160	0.9538
Samp22	0.0363	0.0450	0.0405	0.9064
Samp23	0.0246	0.0580	0.0404	0.9188
Samp24	0.0250	0.0933	0.0438	0.8892
Samp31	0.0094	0.0194	0.0140	0.9718
Samp41	0.0211	0.0380	0.0296	0.9409
Samp42	0.0123	0.0063	0.0080	0.9806
Samp51	0.0398	0.2016	0.0751	0.7751
Samp52	0.0113	0.2921	0.0409	0.7623
Samp53	0.0133	0.5212	0.0338	0.5162
Samp54	0.0158	0.0714	0.0457	0.9086
Samp61	0.0026	0.3253	0.0137	0.7648
Samp71	0.0221	0.2124	0.0437	0.7787
平均	0.0293	0.1416	0.0430	0.8442

样本	Axelsson^[18]	Wang等^[30]	王丽英等^[31]	本文算法
Samp11	0.1076	0.1949	0.1862	0.1609
Samp12	0.0325	0.0402	0.0487	0.0384
Samp21	0.0425	0.0205	0.0174	0.016
Samp22	0.0363	0.0497	0.0460	0.0405
Samp23	0.0400	0.0591	0.0504	0.0404
Samp24	0.0442	0.0634	0.0623	0.0438
Samp31	0.0478	0.0158	0.0203	0.0140
Samp41	0.1391	0.0217	0.0209	0.0296
Samp42	0.0162	0.0107	0.0098	0.0080
Samp51	0.0272	0.0809	0.0818	0.0751
Samp52	0.0307	0.0490	0.0424	0.0409
Samp53	0.0891	0.0346	0.0453	0.0338
Samp54	0.0332	0.0562	0.0508	0.0457
Samp61	0.0208	0.0193	0.0231	0.0137
Samp71	0.0163	0.0542	0.0614	0.0437
平均	0.0482	0.0513	0.0511	0.0430