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Proceedings Paper

Natural neighbors interpolation method for correcting IDW
Author(s): Jia Li; Jiatian Li; Xiaoqing Zuo; Ping Duan
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Paper Abstract

Digital Elevation Model (DEM) interpolation is one of basic functions for spatial description and spatial analysis in GIS and related spatial information fields. Interpolation can be viewed as a function for estimating the heights of unknown points using a set of proper known data. It is a key problem of DEM. Inverse distance weighting (IDW) interpolation is the most commonly used in DEM. The reference points selected by IDW might not be well distributed in space. This leads to the discontinuity problem of interpolated DEM surface and some artifacts might be generated. In order to solve the problem caused by ill-distribution of the number and position of reference points in searching process, this paper put forward a new surface interpolation model about first-order natural neighbor interpolation. The use of first-order natural neighbor interpolation based on TIN can adapt well to poor data distributions because inserting into a point generates a well-defined set of neighbors. In the fitting process, according to range of influence composed by first-order natural neighbor points and the triangle area as weight base of the known point, a non-linear fitting equation can be constructed. Comparative experiments show that this method has higher precision and more practical application value.

Paper Details

Date Published: 16 October 2009
PDF: 8 pages
Proc. SPIE 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining, 74925E (16 October 2009); doi: 10.1117/12.838426
Show Author Affiliations
Jia Li, Kunming Univ. of Science and Technology (China)
Jiatian Li, Kunming Univ. of Science and Technology (China)
Xiaoqing Zuo, Kunming Univ. of Science and Technology (China)
Ping Duan, Kunming Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 7492:
International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining
Yaolin Liu; Xinming Tang, Editor(s)

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