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Proceedings Paper

Robust smooth fitting method for LIDAR data using weighted adaptive mapping LS-SVM
Author(s): Sheng Zheng; Jing Ye; Wenzhong Shi; Changcai Yang
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Paper Abstract

In many spatial analyses and visualizations related to terrain, a high resolution and accurate digital surface model (DSM) is essential. To develop a robust interpolation and smoothing solutions for airborne light detection and ranging (LIDAR) point clouds, we introduce the weighted adaptive mapping LS-SVM to fit the LIDAR data. The SVM and the weighted LS-SVM are introduced to generate DSM for the sub-region in the original LIDAR data, and the generated DSM for this region is optimized using the points located within this region and additional points from its neighborhood. The fitting results are adaptively optimized by the local standard deviation and the global standard deviation, which decide whether the SVM or the weighted LS-SVM is applied to fit the sub-region. The smooth fitting results on synthesis and actual LIDAR data set demonstrate that the proposed smooth fitting method is superior to the standard SVM and the weighted LS-SVM in robustness and accuracy.

Paper Details

Date Published: 5 November 2008
PDF: 10 pages
Proc. SPIE 7144, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: The Built Environment and Its Dynamics, 71442C (5 November 2008); doi: 10.1117/12.812832
Show Author Affiliations
Sheng Zheng, China Three Gorges Univ. (China)
Jing Ye, China Three Gorges Univ. (China)
Wenzhong Shi, The Hong Kong Polytechnic Univ. (Hong Kong, China)
Changcai Yang, China Three Gorges Univ. (China)


Published in SPIE Proceedings Vol. 7144:
Geoinformatics 2008 and Joint Conference on GIS and Built Environment: The Built Environment and Its Dynamics
Lin Liu; Xia Li; Kai Liu; Xinchang Zhang; Xinhao Wang, Editor(s)

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