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


  • X. Pons Universitat Autònoma de Barcelona
  • E. Sevillano Grumets Research Group, CREAF
  • G. Moré Grumets Research Group, CREAF
  • P. Serra Grumets Research Group, CREAF
  • D. Cornford Aston University
  • M. Ninyerola Universitat Autònoma de Barcelona



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


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.


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Author Biographies

X. Pons, Universitat Autònoma de Barcelona

Grumets Research Group, Dep. de Geografia, Universitat Autònoma de Barcelona

D. Cornford, Aston University

Computer Science, Aston University. Birmingham

M. Ninyerola, Universitat Autònoma de Barcelona

Grumets Research Group, Dep. Biologia Animal, Vegetal i Ecologia, Universitat Autònoma de Barcelona


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