Uncertainty and Confidence in Land Cover Classification Using a Hybrid Classifier Approach

Traditional methods of land cover classification and mapping are limited in providing spatial data on the uncertainty of map labels. In this paper, we present a hybrid classifier approach using Decision Tree (DT) and ARTMAP 1 neural network to providing confidence or uncertainty information via majority voting and other rules. The hybrid classifier is tested with AVHRR data to mapping land cover of North America. The two classifiers (DT and ARTMAP) tend to make predictive errors in different contexts. They show 68% agreement in classifying land cover of North America. A set of rules is developed to assign class labels for pixels where the two classifiers disagree. Levels of confidence in the hybrid classification derived from their individual voting (ARTMAP) and probability (DT) are used to assign confidence. The approach outlined in this paper produces two products—a hybrid classification map as well as a confidence map based on the two classification schemes. The hybrid approach seems suitable to tackle a variety of classification problems in remote sensing and may ultimately aid map users in making more informed decisions. Background