DEM fusion concept based on the LS-SVM cokriging method. (2nd October 2019)

• Record Type: Journal Article
• Authors: Setiyoko, Andie; Arymurthy, Aniati Murni; Basaruddin, T
• Is Part Of: International journal of image and data fusion. Volume 10:Number 4(2019)
• Page Start: 244
• Page End: 262
• Publication Date: 2019-10-02
• Subjects: DEM -- data fusion -- cokriging -- LS-SVM; Image processing -- Periodicals; Multisensor data fusion -- Periodicals; Multisensor data fusion; Periodicals; 621.36705
• DOI: 10.1080/19479832.2019.1664647
• Languages: English
• ISSNs: 1947-9832

Abstract

ABSTRACT: 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.