Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 53-56, 2016
https://doi.org/10.5194/isprs-archives-XLI-B1-53-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
02 Jun 2016
A UNIVERSAL DE-NOISING ALGORITHM FOR GROUND-BASED LIDAR SIGNAL
Xin Ma1,2, Chengzhi Xiang1, and Wei Gong1 1State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2Collaborative Innovation Center for Geospatial Technology, Wuhan 430079, China
Keywords: Ground-based lidar, Universal de-noising, Signal segmentation, Dual field-of-view Mie lidar Abstract. Ground-based lidar, working as an effective remote sensing tool, plays an irreplaceable role in the study of atmosphere, since it has the ability to provide the atmospheric vertical profile. However, the appearance of noise in a lidar signal is unavoidable, which leads to difficulties and complexities when searching for more information. Every de-noising method has its own characteristic but with a certain limitation, since the lidar signal will vary with the atmosphere changes. In this paper, a universal de-noising algorithm is proposed to enhance the SNR of a ground-based lidar signal, which is based on signal segmentation and reconstruction. The signal segmentation serving as the keystone of the algorithm, segments the lidar signal into three different parts, which are processed by different de-noising method according to their own characteristics. The signal reconstruction is a relatively simple procedure that is to splice the signal sections end to end. Finally, a series of simulation signal tests and real dual field-of-view lidar signal shows the feasibility of the universal de-noising algorithm.
Conference paper (PDF, 843 KB)


Citation: Ma, X., Xiang, C., and Gong, W.: A UNIVERSAL DE-NOISING ALGORITHM FOR GROUND-BASED LIDAR SIGNAL, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 53-56, https://doi.org/10.5194/isprs-archives-XLI-B1-53-2016, 2016.

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