Distribución espacial de la incertidumbre en mapas de cubiertas obtenidos mediante teledetección

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When combining remote sensing imagery with statistical classifiers to obtain categorical thematic maps it is not usual to provide data about the spatial distribution of the error and uncertainty of the resulting maps. This paper describes, in the context of GeoViQua FP7 project, feasible approaches for methods based on several steps such as hybrid classifiers. Both for “per pixel” and “per polygon” strategies, the proposal is based on the use of the available ground truth, which is used to properly model the spatial distribution of the errors. Results allow mapping the classification success with a very high level of reliability (R2>0,94), providing users a sound knowledge of the accuracy at every area of the map.



Translated title of the contributionSpatial distribution of the uncertainty in land cover maps obtained from remote sensing
Original languageSpanish
Pages (from-to)1-10
Number of pages10
JournalRevista de teledeteccion
Issue number42
StatePublished - Dec 2014

Bibliographic note

Esta revista se publica bajo una Licencia Creative Commons Atribución-NoComercial-SinDerivar 4.0 Internacional Funding: Comisión Europea “QUAlity aware VIsualisation for the Global Earth Observation system of systems (GeoViQua)”, ref. FP7 ENV.2010,4.1,2-2 265178, y del proyecto financiado por el Ministerio de Economía y Competitividad del Gobierno de España “Análisis espaciotemporal de las cubiertas del suelo y del estrés de la vegetación en la P. Ibérica a la luz de medio Siglo (1975-2025) de dinámica climática y sus anomalías (DinaCliVe)”, ref. CGL2012-33927; y ICREA Academia Excellence in Research grant (2011-2015)


  • hybrid classification, Landsat, multivariate linear regression, multivariate logistic regression, spatial distribution of uncertainty and error


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