Point Cloud Filter of Skewness Balance Based on the Guidance of Multispectral Data

Xiaofeng Han; Fengbao Yang; Hong Wei; Dawei Li; Dan Liu

doi:10.3788/LOP55.042801

Journals >Laser & Optoelectronics Progress >Volume 55 >Issue 4 >Page 042801 > Article

Laser & Optoelectronics Progress
Vol. 55, Issue 4, 042801 (2018)

Point Cloud Filter of Skewness Balance Based on the Guidance of Multispectral Data

Xiaofeng Han¹, Fengbao Yang^1、*, Hong Wei¹, Dawei Li¹, and Dan Liu¹

Author Affiliations

¹ 2School of Systems Engineering, University of Reading, Reading, Berkshire RG6 6AU,UK

¹ School of Information and Communication Engineering, North University of China, Taiyuan, Shanxi 0 30051, China

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DOI: 10.3788/LOP55.042801 Cite this Article Set citation alerts

Xiaofeng Han, Fengbao Yang, Hong Wei, Dawei Li, Dan Liu. Point Cloud Filter of Skewness Balance Based on the Guidance of Multispectral Data[J]. Laser & Optoelectronics Progress, 2018, 55(4): 042801 Copy Citation Text

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Fig. 1. Method flow chart

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Fig. 2. Method of spectral similarity matrix generation

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Fig. 3. Comparison of experimental results

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Data	Transducer	Flightaltitude /m	Point clouddensity /m^-2	Coveragearea /km²
Pointcloud	ALS50	500	6.7	2.4
Spectrum	DMC	900	0.08	2.4

Table 1. Data parameters of Vaihingen

Data	Method	Error rateof the firstkind /%	Error rateof the secondkind /%	Totalerror	Totaltime /s
1	Skewnessbalance	1.84	0.76	2.6	1826
	Proposedmethod	2.08	0	2.08	1354
	SVM	1.72	0.67	2.39	3639
2	Skewnessbalance	1.62	1.54	3.16	3022
	Proposedmethod	1.70	0.73	2.43	2396
	SVM	1.26	1.43	2.69	5628
3	Skewnessbalance	2.36	1.23	3.59	2357
	Proposedmethod	2.42	0.78	3.20	2064
	SVM	2.23	1.06	3.29	4382
4	Skewnessbalance	2.09	0.95	3.04	2931
	Proposedmethod	2.03	0.22	2.25	2067
	SVM	1.82	0.87	2.69	5210

Table 2. Comparison of experimental results of different methods

Xiaofeng Han, Fengbao Yang, Hong Wei, Dawei Li, Dan Liu. Point Cloud Filter of Skewness Balance Based on the Guidance of Multispectral Data[J]. Laser & Optoelectronics Progress, 2018, 55(4): 042801

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