An efficient filtering method considering terrain features for large-scale airborne LiDAR point clouds

doi:10.11947/j.AGCS.2025.20240484

Abstract

Abstract:

To solve the problems of low accuracy, slow computing speed and poor accuracy of the existing filtering methods for large-scale point cloud data in complex landscapes, an efficient filtering method considering terrain features for large-scale airborne LiDAR point clouds is proposed in this paper. Firstly, radius filtering and elevation histogram method are used to remove outliers from the raw point cloud. Secondly, a large number of evenly distributed ground seed points are efficiently extracted by combining the sliding grid with the M-estimation sample consistency algorithm. Then, the ground reference surface is constructed using a global weighted finite difference thin plate spline (TPS). Finally, a terrain-adaptive elevation threshold is designed to capture various types of ground points. In order to verify the effectiveness of the proposed method, a large-scale point cloud named OpenGF is used in the experiments. The results show that the average total error and Kappa coefficient of the proposed method are 2.45% and 94.54%, respectively, and its overall performance is much better than those of the 10 representative filtering methods. Moreover, the proposed method has a high computational efficiency.

Key words: point cloud filtering, airborne LiDAR, terrain-adaptive, thin plate spline

CLC Number:

P237

Lianzhong XU, Chuanfa CHEN, Dongxing CHEN, Xingjie WANG, Ziming YANG, Shufan YANG, Zhuangzhuang HONG, Jinda HAO. An efficient filtering method considering terrain features for large-scale airborne LiDAR point clouds[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(6): 1082-1093.

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样本区域	样本特征	地面点数	非地面点数	点云密度/（pts/m²）	平均地形坡度/（°）
Test 1	农村、城市、混合地形	20 599 329	26 233 254	7.09	19.30
Test 2	大型建筑物	3 095 510	3 135 353	5.41	3.77
Test 3	陡坡上的密集植被、梯田	3 602 430	16 161 901	7.19	16.16

样本区域	参数				精度
样本区域	W_s/m	h/m	k	t₀/m	T_Ⅰ/（%）	T_Ⅱ/（%）	T_E/（%）	Kappa/（%）
Test 1	12	1.00	0.10	0.06	1.73	1.84	1.79	96.37
Test 2	90	1.00	0.50	0.01	1.35	6.44	3.91	92.18
Test 3	14	1.00	0.15	0.06	2.69	1.42	1.65	94.54
平均值					1.92	3.23	2.45	94.37
标准差					0.56	2.28	1.03	1.72

样本区域	指标	本文方法	SMRF	PTD	MASF	PMF	MCC	CSF	MHC	SegBF	SBF	MSF
Test 1	总误差	1.79	2.24	5.19	3.21	5.39	3.67	8.57	5.99	5.58	4.82	12.35
Test 1	Kappa系数	96.37	95.45	89.49	93.52	89.00	92.55	82.61	87.91	88.78	90.32	75.56
Test 2	总误差	3.91	6.98	6.35	9.94	8.94	15.53	12.48	15.64	23.90	23.61	17.64
Test 2	Kappa系数	92.18	86.05	87.29	80.12	82.11	69.80	80.27	68.77	52.29	52.88	64.71
Test 3	总误差	1.65	1.93	2.52	2.01	3.47	3.03	5.61	3.26	3.00	4.15	7.57
Test 3	Kappa系数	94.54	93.57	91.47	93.45	88.17	89.55	81.28	89.46	90.37	86.99	75.68
平均值	总误差	2.45	3.72	4.69	5.05	5.93	7.41	8.02	8.30	10.83	10.86	12.52
平均值	Kappa系数	94.37	91.69	89.42	89.03	86.43	83.97	81.39	82.05	77.15	76.73	71.98

样本区域	本文方法	SMRF	PTD	MASF	PMF	MCC	CSF	MHC	SegBF	SBF	MSF
Test 1	7.62	21.60	4.53	135.63	143.50	77.00	27.65	599.36	17.24	3.82	1.18
Test 2	1.87	3.63	0.45	16.46	40.99	1 715.00	3.36	224.97	2.14	0.38	0.26
Test 3	4.25	6.32	1.28	29.83	81.80	71.12	10.90	288.46	7.20	0.78	0.39
平均值	4.58	10.52	2.09	60.64	88.76	621.04	13.97	370.93	8.86	1.66	0.61

样本区域	本文方法	无贡献点①	无贡献点②	无贡献点③
Test 1	1.67	2.13	1.95	1.78
Test 2	4.23	4.95	4.37	5.39
Test 3	1.80	2.31	2.31	1.98