Spatial distribution of the uncertainty in land cover maps obtained from remote sensing

Pons Xavier, E. Sevillano, G. Moré, P. Serra, D. Cornford, M. Ninyerola

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)


© 2014 Asociacion Espanola de Teledeteccion. All rights reserved. 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.
Original languageEnglish
Pages (from-to)1-10
JournalRevista de Teledeteccion
Publication statusPublished - 1 Jan 2014


  • Hybrid classification
  • Landsat
  • Multivariate linear regression
  • Multivariate logistic regression
  • Spatial distribution of uncertainty and error


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