Incorporating uncertainty in the accuracy assessment of land cover maps using fuzzy numbers and fuzzy arithmetic

This paper proposes an effort to include uncertainty in reference databases used to assess the accuracy of land cover maps. Five linguistic levels of confidence in land cover labelling are assigned to each sample observation and converted into fuzzy numbers. This information is introduced in a fuzzy confusion matrix and fuzzy accuracy measures, similar to the global, user's and producer's accuracy, are then derived from the fuzzy confusion matrix using fuzzy arithmetic. These measures consist of fuzzy numbers that incorporate the uncertainty in identifying the reference land cover class of the sample data. Fuzzy accuracy measures can be defuzzified to generate real numbers, enabling the conversion into crisp measures, which allow the comparison with the accuracy results obtained with traditional confusion matrixes. The proposed methodology is tested on a case study. The quality of a map for Continental Portugal, derived from the automatic classification of MERIS images, is evaluated using a reference database generated with the proposed methodology.