Waveform LiDAR signal denoising based on connected domains

doi:10.1007/s12200-017-0747-z

Front. Optoelectron.

2017, Vol. 10

Issue (4) : 388-394 https://doi.org/10.1007/s12200-017-0747-z

RESEARCH ARTICLE

Waveform LiDAR signal denoising based on connected domains

Liyu SUN, Zhiwei DONG, Ruihuan ZHANG, Rongwei FAN, Deying CHEN(

)

National Key Laboratory of Science and Technology on Tunable Laser, Harbin Institute of Technology, Harbin 150001, China

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Abstract

The streak tube imaging light detection and ranging (LiDAR) is a new type of waveform sampling laser imaging radar whose echo signals are stripe images with a high frame rate. In this study, the morphological and statistical characteristics of stripe signals are analyzed in detail. Based on the concept of mathematical morphology denoising, connected domains are constructed in a noise-containing stripe image, and the noise is removed using the difference in connected domains area between signals and noises. It is shown that, for stripe signals, the proposed denoising method is significantly more efficient than Wiener filtering.

Keywords stripe signal connected domain denoising

Corresponding Author(s): Deying CHEN

Just Accepted Date: 30 October 2017 Online First Date: 29 November 2017 Issue Date: 21 December 2017

Cite this article:

Liyu SUN,Zhiwei DONG,Ruihuan ZHANG, et al. Waveform LiDAR signal denoising based on connected domains[J]. Front. Optoelectron., 2017, 10(4): 388-394.

URL:

https://academic.hep.com.cn/foe/EN/10.1007/s12200-017-0747-z
https://academic.hep.com.cn/foe/EN/Y2017/V10/I4/388

Fig.1 Diagram of typical streak tube

Fig.2 Original stripe image

Fig.3 Flow diagram of connected domain method

Fig.4 Connected domain area graph

Fig.5 Denoised results for different threshold area

Fig.6 Denoised results of waveform sampling stripe signals. (a) Denoised result of Wiener filtering; (b) denoised result of connected domain method

Tab.1 Comparison of different denoising methods

Fig.7 Elevation maps of buildings. (a) Original points cloud before denoising; (b) denoised by Wiener filtering; (c) denoised by connected domain method

Fig.8 Elevation maps of flat ground. (a) Original points cloud before denoising; (b) denoised by Wiener filtering; (c) denoised by connected domain method

Tab.2 Dispersion and point density of flat ground in points cloud

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