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Noisy Data Smoothing in DEM Construction Using Least Squares Support Vector Machines
Author(s) -
Chen Chuanfa,
Li Yanyan,
Dai Honglei,
Cao Xuewei
Publication year - 2014
Publication title -
transactions in gis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.721
H-Index - 63
eISSN - 1467-9671
pISSN - 1361-1682
DOI - 10.1111/tgis.12078
Subject(s) - smoothing , computation , computer science , support vector machine , interpolation (computer graphics) , kriging , algorithm , noise (video) , sampling (signal processing) , smoothness , mathematics , artificial intelligence , mathematical optimization , machine learning , computer vision , filter (signal processing) , motion (physics) , mathematical analysis , image (mathematics)
Since spatial datasets are subject to sampling errors, a smoothing interpolation method should be employed to remove noise during DEM construction. Although least squares support vector machines ( LSSVM ) have been widely accepted as a classifier, their effect on smoothing noisy data is almost unknown. In this article, the smoothness of LSSVM was explored, and its effect on smoothing noisy data in DEM construction was tested. In order to improve the ability to deal with large datasets, a local method of LSSVM has been developed, where only the neighboring sampling points around the one to be estimated are used for computation. A numerical test indicated that LSSVM is more accurate than the classical smoothing methods including TPS and kriging, and its error surfaces are more evenly distributed. The real‐world example of smoothing noise inherent in lidar‐derived DEM s also showed that LSSVM has a positive smoothing effect, which is approximately as accurate as TPS . In short, LSSVM with a high efficiency can be considered as an alternative smoothing method for smoothing noisy data in DEM construction.