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Mots RAMEAU représentant le fonds du centre de documentation
géomatique, géodésie, réseaux (géodésie), systèmes de localisation par satellites,
imagerie satellitaire, traitement d'images, photogrammétrie, lasergrammétrie,
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Nouvelle recherche

Article: Article/Communication

Accuracy assessment of digital elevation models using a non-parametric approach / F. Aguilar in International journal of geographical information science IJGIS, vol 21 n° 6-7 (july 2007)

URL article

Public

[article]
inInternational journal of geographical information science IJGIS > vol 21 n° 6-7 (july 2007) . - pp 667 - 686

Titre :	Accuracy assessment of digital elevation models using a non-parametric approach
Type de document :	Article/Communication
Auteurs :	F. Aguilar, Auteur ; M. Aguilar, Auteur ; F. Aguera, Auteur
Année de publication :	2007
Article en page(s) :	pp 667 - 686
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Traitement d'image [Termes IGN] analyse comparative [Termes IGN] données localisées [Termes IGN] estimation statistique [Termes IGN] méthode de Monte-Carlo [Termes IGN] modèle numérique de surface [Termes IGN] qualité des données [Termes IGN] résidu
Résumé :	(Auteur) This paper explores three theoretical approaches for estimating the degree of correctness to which the accuracy figures of a gridded Digital Elevation Model (DEM) have been estimated depending on the number of checkpoints involved in the assessment process. The widely used average-error statistic Mean Square Error (MSE) was selected for measuring the DEM accuracy. The work was focused on DEM uncertainty assessment using approximate confidence intervals. Those confidence intervals were constructed both from classical methods which assume a normal distribution of the error and from a new method based on a non-parametric approach. The first two approaches studied, called Chi-squared and Asymptotic Student t, consider a normal distribution of the residuals. That is especially true in the first case. The second case, due to the asymptotic properties of the t distribution, can perform reasonably well with even slightly non-normal residuals if the sample size is large enough. The third approach developed in this article is a new method based on the theory of estimating functions which could be considered much more general than the previous two cases. It is based on a non-parametric approach where no particular distribution is assumed. Thus, we can avoid the strong assumption of distribution normality accepted in previous work and in the majority of current standards of positional accuracy. The three approaches were tested using Monte Carlo simulation for several populations of residuals generated from originally sampled data. Those original grid DEMs, considered as ground data, were collected by means of digital photogrammetric methods from seven areas displaying differing morphology employing a 2 by 2 m sampling interval. The original grid DEMs were subsampled to generate new lower-resolution DEMs. Each of these new DEMs was then interpolated to retrieve its original resolution using two different procedures. Height differences between original and interpolated grid DEMs were calculated to obtain residual populations. One interpolation procedure resulted in slightly non-normal residual populations, whereas the other produced very non-normal residuals with frequent outliers. Monte Carlo simulations allow us to report that the estimating function approach was the most robust and general of those tested. In fact, the other two approaches, especially the Chi-squared method, were clearly affected by the degree of normality of the residual population distribution, producing less reliable results than the estimating functions approach. This last method shows good results when applied to the different datasets, even in the case of more leptokurtic populations. In the worst cases, no more than 64-128 checkpoints were required to construct an estimate of the global error of the DEM with 95% confidence. The approach therefore is an important step towards saving time and money in the evaluation of DEM accuracy using a single average-error statistic. Nevertheless, we must take into account that MSE is essentially a single global measure of deviations, and thus incapable of characterizing the spatial variations of errors over the interpolated surface. Copyright Taylor & Francis
Numéro de notice :	A2007-265
Affiliation des auteurs :	non IGN
Thématique :	IMAGERIE
Nature :	Article
DOI :	10.1080/13658810601079783
En ligne :	https://doi.org/10.1080/13658810601079783
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=28628

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