Discriminant models for uncertainty characterization in area class change categorization

Discriminant space defining area classes is an important conceptual construct for uncertainty characterization in area-class maps. Discriminant models were promoted as they can enhance consistency in area-class mapping and replicability in error modeling. As area classes are rarely completely separable in empirically realized discriminant space, where class inseparability becomes more complicated for change categorization, we seek to quantify uncertainty in area classes (and change classes) due to measurement errors and semantic discrepancy separately and hence assess their relative margins objectively. Experiments using real datasets were carried out, and a Bayesian method was used to obtain change maps. We found that there are large differences between uncertainty statistics referring to data classes and information classes. Therefore, uncertainty characterization in change categorization should be based on discriminant modeling of measurement errors and semantic mismatch analysis, enabling quantification of uncertainty due to partially random measurement errors, and systematic categorical discrepancies, respectively.