测绘学报 ›› 2014, Vol. 43 ›› Issue (2): 178-192.

• 学术论文 • 上一篇    下一篇

DEM插值参数优选的试验研究

张锦明1,游雄2,万刚3   

  1. 1. 解放军信息工程大学测绘学院
    2. 信息工程大学测绘学院
    3. 郑州解放军信息工程大学测绘学院209号
  • 收稿日期:2012-08-22 修回日期:2013-01-23 出版日期:2014-02-20 发布日期:2014-02-28
  • 通讯作者: 张锦明 E-mail:zjmwh@vip.sohu.com
  • 基金资助:

    基于非专业弱相关照片的三维空间环境快速重建理论与技术研究

Experimental Research on Optimization of DEM Interpolation Parameters

  • Received:2012-08-22 Revised:2013-01-23 Online:2014-02-20 Published:2014-02-28

摘要:

插值参数是构成插值算法的基本元素,不同的插值参数产生不同的插值精度,甚至存在巨大差异。但是对于普通用户来说,选择合适的插值参数是困难的,最终导致插值参数选择的随意性。在插值算法相关内容的研究中,使用不合适的插值算法或插值参数可能存在潜在的严重后果,甚至得到完全相反的实验结论。因此,本文根据插值算法最优权重确定方法的差异,选取反距离加权插值算法、径向基函数插值算法和普通克里格插值算法的相关插值参数,进行插值参数的“优选”研究。首先根据插值参数对插值精度的不同影响,选择相关插值参数作为实验研究对象;然后选择六种不同地貌类型地区的稀疏分布的离散采样数据作为实验数据源,运用交叉验证法、相关分析、趋势面分析和方差分析等一系列实验方法,系统研究并给出实验插值参数的“最优”取值区间, 消除插值参数选择的随意性,更好地指导DEM建模的运用。

关键词: 插值参数, 搜索方式, 核函数, 交叉验证法, 相关分析, 趋势面分析, 方差分析

Abstract:

Interpolation parameter is one of the basic elements of the DEM interpolation method. Different interpolation parameters produce different interpolation precisions and, the differences may be huge by using various interpolation parameters. But for ordinary users, it is difficult to select an appropriate set of interpolation parameters during the interpolation process. In the related researches on interpolation method, using the appropriate interpolation algorithm or interpolation parameters may lead to potentially serious consequences, even completely opposite experimental conclusions. Therefore, based on the difference of the weight determination method in interpolation algorithm, this paper selected related parameters with inverse distance weighted interpolation method, radial basis functions interpolation method and ordinary kridging interpolation method, and focused on selecting "Optimized" interpolation parameters in interpolation method. First according to the different effects of the interpolation parameters on interpolation accuracy, took the relevant interpolation parameters as the experimental object. Then chosen six different regions of landform types of the sparse distribution and discrete sampling data as data source, and applied a series of methods such as cross validation, correlation analysis, trend surface analysis and variance of analysis to systematic research on choosing optimized DEM interpolation parameters and proposed the uncertainty of the interpolation parameters of the "optimum" value range.

Key words: Interpolation Parameter, Search Type, Kernel Function, Cross Validation, Correlate Analysis, Trend Surface Analysis, Analysis of Variance

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