Journal of Geo-information Science >
A Hierarchical RBF Interpolation Method Based on Local Optimal Shape Parameters
Received date: 2014-04-22
Request revised date: 2014-05-20
Online published: 2015-03-10
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As an accurate spatial interpolation method for data in arbitrary dimensions, Radial Basis Function (referred RBF), was particularly suitable for Digital Elevation Model (referred DEM) interpolation with respect to complex terrain that no assumption is needed for the experiment data. But the interpolation model would become difficult to solve when the number of points, whose elevation is already known, used to construct RBF interpolation model is too large. This is due to the reason that the inversed RBF interpolation matrix would become too huge or too slow to be solved. To address this issue, the hierarchical RBF interpolation method based on local optimal shape parameters, was proposed in this paper and the DEM was interpolated and reconstructed in the experiment. The interpolation procedure was described as follows: first, set the minimum point number in the tree node sub-regions of the study area and define the overlap rate between the adjacent child node sub-regions. Then, construct the regional binary tree recursively from top to bottom, that means the study area was firstly divided from a complete area into two small overlapped regions, and each region could be taken as the child nodes of the binary tree. Second, use the Leave One Out Cross Validation (referred LOOCV) method to calculate the optimal shape parameters in the leaf node regions of the binary tree. As the point distributions in each sub-region are different from each other, as well as the elevation properties, it would lead to different optimal shape parameters. Next, establish the optimal RBF interpolation model, i.e. calculate the linear combination coefficients for each RBF node in the interpolation model with the optimal shape parameter. Third, calculate the elevations of the unknown points in the leaf node regions and get the elevation values using the weighted average method according to the principle of Partition of Unity. The weight of the unknown point in the child node sub-region is calculated using the distance to the center point of the sub-region. Solve the interpolation problem from the bottom to the top recursively to get the final elevation values of the unknown points. Experiments was carried out by using the DEM in some area of Yunnan Province, and the results showed that RBF hierarchical interpolation method with local optimal shape parameters had good stability and high accuracy when DEM was reconstructed from random distributed spatial elevation points, thus it can be taken as an reliable interpolation method.
Key words: shape parameters; DEM; RBF; hierarchical interpolation
LV Haiyang , SHENG Yehua , DUAN Ping , ZHANG Siyang , LI Jia . A Hierarchical RBF Interpolation Method Based on Local Optimal Shape Parameters[J]. Journal of Geo-information Science, 2015 , 17(3) : 260 -267 . DOI: 10.3724/SP.J.1047.2015.00260
Fig. 1 Weight change in leaf area图1 叶子节点区域权重变化 |
Fig. 2 Interpolation process for the unknown point图2 待插值点插值过程 |
Tab. 1 Some RBF basis functions表1 几种常见的RBF基函数 |
基函数 | 表达式 |
---|---|
Gaussians | |
Multiquadric | |
Inverse Multiquadric | |
Thin Plate Spline |
注:α为形态参数,控制基函数的平滑程度,r为待插值点与已知点之间的径向距(2-范数) |
Fig. 3 The study area图3 研究区域DEM |
Tab.2 Topographic factors statistics in study area表2 研究区域地形因子统计 |
最小高程(m) | 最大高程(m) | 平均高程(m) | 标准差(m) | 平均坡度(°) |
---|---|---|---|---|
1245 | 2585 | 2010 | 252 | 24.5 |
Fig. 4 10 000 random sample points图4 10 000个随机高程采样点 |
Fig. 5 Interpolation results comparison图5 插值结果对比 |
Fig. 6 The local enlarged view of the original DEM and interpolation results图6 原始数据与插值结果局部放大 |
Tab. 3 Error statistic of different interpolation methods (m)表3 不同方法插值结果分析(m) |
插值方法 | 最大误差 | 最小误差 | 平均误差 | 均方根误差 |
---|---|---|---|---|
IDW | 137.4158 | 7.2966×10-4 | 19.0169 | 25.4318 |
CSRBF | 302.3613 | 1.6825×10-4 | 9.7419 | 16.5656 |
Pouderoux方法 | 139.1777 | 3.9567×10-5 | 9.7295 | 13.6998 |
本文方法 | 74.8113 | 1.0037×10-5 | 7.2496 | 10.1841 |
The authors have declared that no competing interests exist.
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