Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12597
Title: Robust smooth fitting method for LIDAR data using weighted adaptive mapping LS-SVM
Authors: Zheng, S
Ye, J
Shi, W 
Yang, C
Keywords: Adaptive optimization
Digital surface model (DSM)
Light detection and ranging (LIDAR)
Support vector machine
Weighted least squares SVM (LS-SVM)
Issue Date: 2008
Publisher: SPIE-International Society for Optical Engineering
Source: Proceedings of SPIE : the International Society for Optical Engineering, 2008, v. 7144, 71442C How to cite?
Journal: Proceedings of SPIE : the International Society for Optical Engineering 
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.
Description: Geoinformatics 2008 and Joint Conference on GIS and Built Environment: The Built Environment and Its Dynamics, Guangzhou, 28-29 June 2008
URI: http://hdl.handle.net/10397/12597
ISSN: 0277-786X
EISSN: 1996-756X
DOI: 10.1117/12.812832
Appears in Collections:Conference Paper

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