Adaptive mapped least squares SVM-based smooth fitting method for DSM generation of LIDAR data

Abstract

This paper presents an adaptive mapped least squares support vector machine (LS-SVM)-based smooth fitting method for DSM generation of airborne light detection and ranging (LIDAR) data. The LS-SVM is 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 neighbourhood. The basic principles of differential geometry are applied to derive the general equations (such as gradients and curvatures) for topographic analysis of the generated DSM. The smooth fitting results on simulated and actual LIDAR datasets demonstrate that the proposed smooth fitting method performs well in terms of the quality evaluation indexes obtained, and is superior to the radial basis function (fastRBF) and triangulation methods in computation efficiency, noise suppression and accurate DSM generation.

Acknowledgements

This work was supported by funds from the Research Grants Council of Hong Kong SAR (project no. PolyU5153/04E), Hong Kong Polytechnic University (project no. PolyU 5254/05E, A-PG47) and, in part, from the China National Natural Science Fund (project no. 60875009).