Robust smooth fitting method for LIDAR data using weighted adaptive mapping LS-SVM

Sheng Zheng, Jing Ye, Wen Zhong Shi, Changcai Yang

Research output: Journal article publicationConference articleAcademic researchpeer-review

1 Citation (Scopus)


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.
Original languageEnglish
Article number71442C
JournalProceedings of SPIE - The International Society for Optical Engineering
Publication statusPublished - 1 Dec 2008
EventGeoinformatics 2008 and Joint Conference on GIS and Built Environment: The Built Environment and Its Dynamics - Guangzhou, China
Duration: 28 Jun 200829 Jun 2008


  • Adaptive optimization
  • Digital surface model (DSM)
  • Light detection and ranging (LIDAR)
  • Support vector machine
  • Weighted least squares SVM (LS-SVM)

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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