Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/14195
Title: Robust methods for assessing the accuracy of linear interpolated DEM
Authors: Wang, B
Shi, W 
Liu, E
Keywords: Confidence interval
DEM accuracy
Interpolation residuals
Monte carlo simulation
Robust estimation
Issue Date: 2015
Publisher: Elsevier
Source: International Journal of applied earth observation and geoinformation, 2015, v. 34, no. 1, p. 198-206 How to cite?
Journal: International Journal of Applied Earth Observation and Geoinformation 
Abstract: Methods for assessing the accuracy of a digital elevation model (DEM) with emphasis on robust methods have been studied in this paper.Based on the squared DEM residual population generated by the bi-linear interpolation method, three average-error statistics including (a) mean, (b) median, and (c) M-estimator are thoroughly investigated for measuring the interpolated DEM accuracy. Correspondingly, their confidence intervals are also constructed for each average error statistic to further evaluate the DEM quality. The first method mainly utilizes the student distribution while the second and third are derived from the robust theories. These innovative robust methods possess the capability of counteracting the outlier effects or even the skew distributed residuals in DEM accuracy assessment. Experimental studies using Monte Carlo simulation have commendably investigated the asymptotic convergence behavior of confidence intervals constructed by these three methods with the increase of sample size. It is demonstrated that the robust methods can produce more reliable DEM accuracy assessment results compared with those by the classical t-distribution-based method. Consequently, these proposed robust methods are strongly recommended for assessing DEM accuracy, particularly for those cases where the DEM residual population is evidently non-normal or heavily contaminated with outliers.
URI: http://hdl.handle.net/10397/14195
ISSN: 1569-8432
DOI: 10.1016/j.jag.2014.08.012
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