Noisy Data Smoothing in DEM Construction Using Least Squares Support Vector Machines
Noisy Data Smoothing in DEM Construction Using Least Squares Support Vector Machines
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DOI：
10.1111/tgis.12078
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发表时间：
2014-12
影响因子：
2.4
通讯作者：
Chuanfa Chen;Yanyan Li;Honglei Dai;Xuewei Cao
中科院分区：
文献类型：
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作者：
Chuanfa Chen;Yanyan Li;Honglei Dai;Xuewei Cao
由于空间数据集容易出现采样误差，因此在 DEM 构建过程中应采用平滑插值方法去除噪声。尽管最小二乘支持向量机（LSSVM）已被广泛接受作为分类器，但它们对平滑噪声数据的影响几乎未知。本文探讨了LSSVM的平滑性，并测试了其在DEM构建中平滑噪声数据的效果。为了提高处理大数据集的能力，提出了一种LSSVM局部方法，仅使用待估计点周围的相邻采样点进行计算。数值试验表明，LSSVM比TPS、克里金等经典平滑方法精度更高，且误差面分布更均匀。激光雷达衍生的 DEM 中固有的平滑噪声的现实示例也表明，LSSVM 具有积极的平滑效果，其精度大约与 TPS 一样。简而言之，LSSVM 具有高效率，可以被认为是 DEM 构建中平滑噪声数据的替代平滑方法。
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 DEMs 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.