Digital Elevation Model Generation in LiDAR Point Cloud Based on Cloth Simulation Algorithm

Fan Zhang; Huashan Li; Tao Jiang

doi:10.3788/LOP57.130104

Journals >Laser & Optoelectronics Progress >Volume 57 >Issue 13 >Page 130104 > Article

Laser & Optoelectronics Progress
Vol. 57, Issue 13, 130104 (2020)

Digital Elevation Model Generation in LiDAR Point Cloud Based on Cloth Simulation Algorithm

Fan Zhang^1、*, Huashan Li², and Tao Jiang^1、**

Author Affiliations

¹School of Surveying and Mapping Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590, China

²CCCC Southern China Surveying & Mapping Technology Co., Ltd., Guangzhou, Guangdong 510221, China;

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DOI: 10.3788/LOP57.130104 Cite this Article Set citation alerts

Fan Zhang, Huashan Li, Tao Jiang. Digital Elevation Model Generation in LiDAR Point Cloud Based on Cloth Simulation Algorithm[J]. Laser & Optoelectronics Progress, 2020, 57(13): 130104 Copy Citation Text

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Fig. 1. Schematic diagram of the cloth simulation algorithm

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Fig. 2. Representative terrain of 6 groups. (a) Original point cloud data; (b) corresponding image

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Fig. 3. Flow chart of technical

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Fig. 4. Six groups of ground points and non-ground points extracted by CSF algorithm. (a) Ground; (b) non-ground

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Fig. 5. Accuracy evaluation. (a) DEM raster generated by ground points; (b) evaluation curve of fitting accuracy

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Type	Data	Area /km×km	Geomorphic feature	Real two kinds of point
Type	Data	Area /km×km	Geomorphic feature	Ground	Non-ground
Rural	sample 1	0.47×0.45	mixture of vegetation and building	1459772	1490737
Urban	sample 2	0.63×0.61	dense buildings, mixed vegetation	612550	480565
Rural	sample 3	1.04×0.45	river bank vegetation	247750	55829
Coteau	sample 4	1.18×1.18	housing mountain replacement, complex terrain	4888419	2825276
Coteau	sample 5	0.58×0.40	slopes are densely vegetated and steep	1867973	2451113
Rural	sample 6	0.32×0.30	low house is connected with a gentle slope	570986	762549

Table 1. Geomorphic features of sample data

Data	Geomorphic feature	RI	ST
Sample 1	gentle slope	2	T
Sample 2	flat topography	1	F
Sample 3	gentle slope	2	T
Sample 4	gradual slope	2	T
Sample 5	high steep	3	T
Sample 6	gentle slope	2	T

Table 2. RI and steep slope treatment in different terrain areas

Real twokinds of point	CSF algorithm		Sum
Real twokinds of point	Ground	Non-ground	Sum
Ground	a	b	e=a+b
Non-ground	c	d	f=c+d
Sum	g=a+c	h=b+d	n=a+b+c+d

Table 3. Cross table

Data	GR /m	H_cc /m	X_TypeI /%	X_TypeII /%	X_Total /%	X_Kappa
Sample 1	0.5	0.4	6.22	9.76	8.01	0.840
		0.5	3.82	10.20	7.04	0.859
		0.6	2.50	10.50	6.55	0.869
		0.7	1.82	10.70	6.32	0.874
		0.8	1.40	10.90	6.23	0.875
		0.9	1.20	11.10	6.21	0.876
		1.0	1.08	11.22	6.20	0.876
Sample 2	0.4	0.3	0.70	4.20	2.30	0.954
		0.4	0.50	5.10	2.50	0.948
		0.5	0.40	5.80	2.80	0.943
		0.6	0.30	6.50	3.00	0.938
		0.7	0.30	7.00	3.20	0.936
		0.8	0.20	7.60	3.50	0.928
Sample 3	0.5	0.7	2.09	7.81	3.14	0.896
		0.8	1.90	8.00	2.99	0.900
		0.9	1.68	8.19	2.87	0.904
		1.0	1.50	8.39	2.79	0.906
		1.1	1.40	8.60	2.70	0.908
		1.2	1.30	8.80	2.70	0.910
		1.3	1.19	9.14	2.60	0.910
		1.4	1.10	9.45	2.60	0.910
Sample 4	0.5	0.5	1.50	15.90	6.70	0.851
		0.6	1.20	16.40	6.80	0.850
		0.7	1.00	16.80	6.80	0.848
Sample 5	0.3	0.4	1.67	6.84	4.61	0.907
		0.5	0.90	7.19	4.44	0.910
		0.6	0.60	7.27	4.41	0.911
		0.7	0.40	7.48	4.46	0.910
		0.8	0.35	7.71	4.53	0.909
		0.9	0.26	7.99	4.65	0.906
		1.0	0.19	8.30	4.79	0.903
Sample 6	0.5	0.3	4.10	3.30	3.70	0.925
		0.4	2.90	3.80	4.20	0.930
		0.5	2.10	4.30	3.30	0.932
		0.6	1.50	4.70	3.40	0.932
		0.7	1.10	5.20	3.40	0.930

Table 4. Three error values and Kappa coefficients obtained by different H_cc

Data	X_TypeI /%	X_TypeII /%	X_Total /%	X_Kappa
Sample 1	2.58	10.63	6.65	0.867
Sample 2	0.40	6.03	2.88	0.941
Sample 3	1.79	8.36	2.99	0.900
Sample 4	1.23	16.37	6.77	0.850
Sample 5	0.62	7.54	4.56	0.908
Sample 6	2.34	4.26	3.60	0.930

Table 5. Average value of filter error and Kappa coefficient

Fan Zhang, Huashan Li, Tao Jiang. Digital Elevation Model Generation in LiDAR Point Cloud Based on Cloth Simulation Algorithm[J]. Laser & Optoelectronics Progress, 2020, 57(13): 130104

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