Incorporating the uncertainty of linguistic-scale reference data to assess accuracy of land-cover maps using fuzzy intervals

The reference classifications that serve as the fundamental basis of accuracy assessment of land-cover maps are subject to uncertainty. A fuzzy interval approach is proposed in which linguistic-scale labels assigned to each land-cover class at each sample observation are converted to fuzzy intervals. These fuzzy intervals are then used to produce a fuzzy confusion matrix from which fuzzy thematic accuracy measures analogous to overall, user's, and producer's accuracy are produced. An advantage of this methodology is that it employs a practical and relatively simple reference labelling protocol (the linguistic scale) that accounts for reference database uncertainty and information on the percentage of the pixel covered by a land-cover class incorporating this uncertainty in fuzzy accuracy measures, providing an analysis that is readily interpretable because of similarity to the familiar confusion matrix approach. The fuzzy accuracy measures can be defuzzified to provide simplified accuracy measures analogous to the thematic accuracy measures derived from traditional (i.e. crisp classification) confusion matrices. The proposed methodology is illustrated using a case study in which the accuracy assessment of a land-cover map for Continental Portugal derived from Medium Resolution Imaging Spectrometer (MERIS) images is made.