Segmentation-Based Filtering of Airborne LiDAR Point Clouds by Progressive Densification of Terrain Segments

Filtering is one of the core post-processing steps for Airborne Laser Scanning (ALS) point clouds. A segmentation-based filtering (SBF) method is proposed herein. This method is composed of three key steps: point cloud segmentation, multiple echoes analysis, and iterative judgment. Moreover, the thi...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2014-02, Vol.6 (2), p.1294-1326
Main Authors: Lin, Xiangguo, Zhang, Jixian
Format: Article
Language:English
Subjects:
airborne LiDAR
Algorithms
Errors
Filtering
Filtration
Judgments
Lidar
Methods
object-based point cloud analysis
point cloud segmentation
Post-processing
progressive TIN densification
PTD
Segments
Three dimensional models
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Summary:Filtering is one of the core post-processing steps for Airborne Laser Scanning (ALS) point clouds. A segmentation-based filtering (SBF) method is proposed herein. This method is composed of three key steps: point cloud segmentation, multiple echoes analysis, and iterative judgment. Moreover, the third step is our main contribution. Particularly, the iterative judgment is based on the framework of the classic progressive TIN (triangular irregular network) densification (PTD) method, but with basic processing unit being a segment rather than a single point. Seven benchmark datasets provided by ISPRS Working Group III/3 are utilized to test the SBF algorithm and the classic PTD method. Experimental results suggest that, compared with the PTD method, the SBF approach is capable of preserving discontinuities of landscapes and removing the lower parts of large objects attached on the ground surface. As a result, the SBF approach is able to reduce omission errors and total errors by 18.26% and 11.47% respectively, which would significantly decrease the cost of manual operation required in post-processing.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs6021294