Abstract:
We present a rationale and method for representing the vagueness in taxonomic class definitions in cases where classes are described by a set of characteristics, such as those sometimes used as the basis for land-cover category discrimination. We further describe methods to estimate the semantic similarity between any two classes by calculating semantic similitude metrics based on such parameterized class definitions. Our working hypothesis is that a large similitude would predict categories that will be more prone to confusion and hence image or map misclassification. We use two different existing data sets to demonstrate and evaluate the method, and the results support our original hypothesis. Consequently, we argue that classification schemes that are based on parameterized definitions could be assessed for problematic categories during their construction using our approach, and thus, enabling the identification of a thematic vagueness component to supplement the more traditional statistical measures derived from the error matrix.
