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A novel denoising algorithm for photon-counting laser data based on LDBSCAN
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Paper Abstract

In this paper, a new single photon laser data processing method is proposed and coarse -to-fine denoising strategy is adopted. Global and piecewise denoising based on the frequency histogram of photon elevation is the first step and direction self-adaptive fine denoising is the next which calculates density value, and uses self-adaptive elliptical LDBSCAN (A Local-Density Based Spatial Clustering Algorithm with Noise) algorithm to effectively remove the noise distributed around the signal segment. Experiments using MABEL (Multiple Altimeter Beam Experimental Lidar) data is implemented and the results validate the proposed algorithm which can effectively extract signal photons from high background noise, and has more reliable results than MABEL official to some extent.

Paper Details

Date Published: 18 December 2019
PDF: 14 pages
Proc. SPIE 11333, AOPC 2019: Advanced Laser Materials and Laser Technology, 113331B (18 December 2019); doi: 10.1117/12.2547964
Show Author Affiliations
Dongping Xie, Liaoning Technical Univ. (China)
Land Satellite Remote Sensing Application Ctr. (China)
Guoyuan Li, Land Satellite Remote Sensing Application Ctr. (China)
Jianmin Wang, Liaoning Technical Univ. (China)
Zhenming Wang, Land Satellite Remote Sensing Application Ctr. (China)
Wuhan Univ. (China)
Fanghong Ye, Land Satellite Remote Sensing Application Ctr. (China)
Wuhan Univ. (China)
Xiongdan Yang, Liaoning Technical Univ. (China)
Land Satellite Remote Sensing Application Ctr. (China)


Published in SPIE Proceedings Vol. 11333:
AOPC 2019: Advanced Laser Materials and Laser Technology
Pu Zhou; Jian Zhang; Wenxue Li; Shibin Jiang; Takunori Taira, Editor(s)

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