A Noise Removal Algorithm Based on OPTICS for Photon-Counting LiDAR Data

Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) shows great potential for forest height retrieval. However, there are abundant noise photons in the ICESat-2 data, which make the accurate extraction of global forest heights challenging. In this letter, a novel algorithm based on the clustering...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters Vol. 18; no. 8; pp. 1471 - 1475
Main Authors: Zhu, Xiaoxiao, Nie, Sheng, Wang, Cheng, Xi, Xiaohuan, Wang, Jinsong, Li, Dong, Zhou, Hangyu
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.08.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
and Land Elevation Satellite-2 (ICESat-2)
Cloud
Clustering
Clustering algorithms
Clustering methods
Data
Distance
Elevation
Forestry
Ice
Laser radar
Lidar
Noise
noise removal
Optics
ordering points to identify the clustering structure (OPTICS)
photon-counting Light Detection and Ranging (LiDAR) (PCL)
Photonics
Photons
Removal
Shape
ISSN:1545-598X, 1558-0571
Online Access:Get full text
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Summary:Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) shows great potential for forest height retrieval. However, there are abundant noise photons in the ICESat-2 data, which make the accurate extraction of global forest heights challenging. In this letter, a novel algorithm based on the clustering method of ordering points to identify the clustering structure (OPTICS) was proposed to remove noise photons. First, we modified the circular shape of the search area in the OPTICS algorithm to an elliptical shape. Second, a distance ordering of all photons was generated using the modified OPTICS algorithm. Finally, signal photons were effectively detected using distance thresholds set by the Otsu method. To evaluate the algorithm performance, both the simulated and real ICESat-2 data were applied to our proposed algorithm. In addition, we compared our algorithm with another noise removal algorithm based on the modified density-based spatial clustering of applications with noise (DBSCAN). The results show that our algorithm works well in distinguishing the signal and noise photons as indicated by high <inline-formula> <tex-math notation="LaTeX">F </tex-math></inline-formula> values. Compared with the modified DBSCAN, our algorithm performs better in filtering out noise photons regardless of the simulated or real ICESat-2 data sets. In addition, the results also indicate that our algorithm is robust because it is insensitive to the clustering parameters. Overall, the new proposed algorithm is effective for removing noise photons in the ICESat-2 data.
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ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2020.3003191