5 November 2008 Robust smooth fitting method for LIDAR data using weighted adaptive mapping LS-SVM
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Proceedings Volume 7144, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: The Built Environment and Its Dynamics; 71442C (2008) https://doi.org/10.1117/12.812832
Event: Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Geo-Simulation and Virtual GIS Environments, 2008, Guangzhou, China
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.
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Sheng Zheng, Sheng Zheng, Jing Ye, Jing Ye, Wenzhong Shi, Wenzhong Shi, Changcai Yang, Changcai Yang, } "Robust smooth fitting method for LIDAR data using weighted adaptive mapping LS-SVM", 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; https://doi.org/10.1117/12.812832
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