Impacts of ignorance on the accuracy of image classification and thematic mapping

Thematic maps are often derived from remotely sensed imagery via a supervised image classification analysis. The training and testing stages of a supervised image classification may proceed ignorant of the presence of some classes in the region to be mapped. This violates the assumption of an exhaustively defined set of classes that is often made in classification analyses. In such circumstances, the overall accuracy of a thematic map produced by the application of a trained classifier will be less than the accuracy of the classification of the test set by the same classifier. This situation arises because the cases of an untrained class can normally only be commissioned into the set of trained classes. Simple mathematical relationships between classification and map accuracy are shown for assessments of overall, user's and producer's accuracy. For example, it is shown that in a simple scenario the accuracy of a thematic map is less than that of a classification, scaling as a function of the abundance of the untrained class(es). Impacts on other estimates made from thematic maps, such as class areal extent, are also briefly discussed. When using a thematic map, care is needed in interpreting and using classification accuracy assessments as sometimes they may not reflect properties of the map well.