Fuzzy set approach to assessing similarity of categorical maps

For the evaluation of results from remote sensing and high-resolution models it is often necessary to assess the similarity of sets of maps. This describes a method to compare raster maps of categorical data. The applies fuzzy set theory and involves both fuzziness of location and of category. The fuzzy comparison yields a map, which specifies for cell the degree of similarity on a scale of 0 to 1. Besides this spatial of similarity also an overall value for similarity is derived. This corrects the cell-average similarity value for the expected similarity. It be considered the fuzzy equivalent of the Kappa statistic and is therefore KFuzzy. A hypothetical case demonstrates how the comparison method minor changes and fluctuations within patterns from major. Finally, a practical case illustrates how the method can be useful in a process.