Interpolation Parameters in Inverse Distance-Weighted Interpolation Algorithm on DEM Interpolation Error

Zhiqiang Liu,Bo Xu,Xiaomei Hu,Bo Cheng

doi:10.1155/2021/3535195

Zhiqiang Liu, Bo Xu + Show 2 more

Open Access

https://doi.org/10.1155/2021/3535195

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Abstract

Although DEM occupies an important basic position in spatial analysis, so far, the quality of DEM modeling has still not reached a satisfactory accuracy. This research mainly discusses the influence of interpolation parameters in the inverse distance-weighted interpolation algorithm on the DEM interpolation error. The interpolation parameters to be studied in this paper are the number of search points, the search direction, and the smoothness factor. In order to study the optimization of IDW parameters, the parameters that have uncertain effects on DEM interpolation are found through analysis, such as the number of search points and smoothing factor. This paper designs an experiment for the optimization of the interpolation parameters of the polyhedral function and finds the optimal interpolation parameters through experimental analysis. Of course, the “optimum” here is not the only one, but refers to different terrain areas, which makes the interpolation results relatively good. The selection of search points will be one of the research focuses of this article. After determining the interpolation algorithm, the kernel function is also one of the important factors that affect the accuracy of DEM. The value of the smoothing factor in the kernel function has always been the focus of DEM interpolation research. Different terrains, different interpolations, and functions will have different optimal smoothing factors. The search direction is to ensure that the sampling points are distributed in all directions when the sampling points are sparse and to improve the contribution rate of the sampling points to the interpolation points. The selection of search shape is to improve computing efficiency and has no effect on DEM accuracy; the search radius is mainly controlled by the number of search points, and there are two methods: adaptive search radius and variable length search radius. When the weight coefficient k = 1 , 2 , 3 , 4 , the number of sampling points involved in the interpolation calculation is different, and the error in the residual varies greatly, and both increase with the increase of the number of sampling points in the parameter interpolation calculation. This research will help improve the quality evaluation of DEM.

Highlights

digital elevation model (DEM) error comes from two aspects: data error and approximation error
In order to study the optimization of inverse distance-weighted (IDW) parameters, the parameters that have uncertain effects on DEM interpolation are found through analysis, such as the number of search points and smoothing factor
The reasonable selection of interpolation function is one of the methods to improve the accuracy of DEM

Summary

Introduction

DEM error comes from two aspects: data error and approximation error. Most of the existing error models only take into account one aspect of the error, so they cannot truly and objectively describe the local pixel accuracy of DEM. The DEM error is comprehensive analysis and modeling is the unavoidable tasks of DEM error model. Interpolation method is a core issue in digital elevation model. The interpolation method always plays an important role in the DEM production process. In accuracy evaluation and accuracy analysis, interpolation algorithms often play an important role. The research of DEM interpolation algorithm has practical significance

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