DTM Generation from LIDAR Data using Skewness Balancing

Light detection and ranging (LIDAR) data for terrain and land surveying has contributed to many environmental, engineering and civil applications. However, the analysis of digital surface models (DSMs) from complex LIDAR data is still challenging. Commonly, the first task to investigate LIDAR data p...

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Bibliographic Details
Published in18th International Conference on Pattern Recognition (ICPR'06) Vol. 1; pp. 566 - 569
Main Authors Bartels, M., Hong Wei, Mason, D.C.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2006
Subjects
Clouds
Data engineering
Filtering algorithms
Floods
Gaussian distribution
Global Positioning System
Laser radar
Pulse measurements
Surface morphology
Vegetation mapping
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Summary:Light detection and ranging (LIDAR) data for terrain and land surveying has contributed to many environmental, engineering and civil applications. However, the analysis of digital surface models (DSMs) from complex LIDAR data is still challenging. Commonly, the first task to investigate LIDAR data point clouds is to separate ground and object points as a preparatory step for further object classification. In this paper, the authors present a novel unsupervised segmentation algorithm - skewness balancing - to separate object and ground points efficiently from high resolution LIDAR point clouds by exploiting statistical moments. The results presented in this paper have shown its robustness and its potential for commercial applications
ISBN:0769525210
9780769525211
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2006.463