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首页> 外文期刊>International journal of image and data fusion >DEM fusion concept based on the LS-SVM cokriging method
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DEM fusion concept based on the LS-SVM cokriging method

机译：基于LS-SVM协同克里金法的DEM融合概念

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摘要

Data fusion from two sources of data could develop better output since the process may minimise any inherent disadvantages of the data. Cokriging data fusion requires a semivariogram fitting process, which is an important step for weight determination in the fusion process. The traditional method of cokriging fusion usually applies a specific model of semivariogram fitting based on the available options, such as circular or tetraspherical. This research aims to fuse height point data from two different sources using ordinary kriging based on LS-SVM regression, which is applied to the semivariogram fitting process. The data used are height points generated from stereo satellite imagery, GPS measurement, and topographic map points to generate DEMs. The research experiment begins by calculating the semivariogram model for all the data, and then the fitting process is performed by applying the same approach of functional approach for both sets of data. The following process is an ordinary cokriging interpolation, whose results are analysed and compared to the ordinary kriging interpolation. The experiment results prove that the ordinary cokriging fusion process could reduce interpolation error. The LS-SVM approach offers better precision in the semivariogram modelling by determining more precise weight calculation for cokriging fusion process.
机译：来自两个数据源的数据融合可以产生更好的输出，因为该过程可以最大程度地减少数据的任何固有缺点。共克里格数据融合需要半变异函数拟合过程，这是确定融合过程中权重的重要步骤。 Cokriging融合的传统方法通常基于圆形或四球形等可用选项应用特定的半变异函数拟合模型。这项研究旨在使用基于LS-SVM回归的普通克里金法融合来自两个不同来源的高度点数据，并将其应用于半变异函数拟合过程。所使用的数据是从立体卫星图像，GPS测量生成的高度点，以及用于生成DEM的地形图点。研究实验首先从计算所有数据的半变异函数模型开始，然后通过对两组数据应用相同的函数方法进行拟合过程。以下过程是普通的克里格插值，将其结果进行分析并与普通克里格插值进行比较。实验结果表明，普通的共克里金融合过程可以减小插值误差。 LS-SVM方法通过确定用于cokriging融合过程的更精确的权重计算，从而在半变异函数建模中提供了更高的精度。

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