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In recent years, machine learning and data mining methods have become increasingly common in remote sensing applications. One area in which such techniques are particularly useful is classification of remotely sensed data for land cover and vegetation mapping applications. In this paper, we describe new methods to include available information (domain knowledge) in supervised classification of land cover using high dimensional remote sensing observations. specifically, land cover and vegetation classification schemes are generally designed for ecological or land use applications. As a result, the classes of interest are often poorly separable in the multi-spectral or multi-temporal feature space provided by remote sensing. In many cases, ancillary data sources can provide useful information to help distinguish between problematic classes. However, available methods for including ancillary data sources, such as the use of prior probabilities in maximum likelihood classification, are often problematic in practice. This paper presents a method for incorporating prior probabilities in remote sensing-based land cover classification using a supervised decision tree classification algorithm. The method exploits recent theory from the domain of statistics and machine learning that allows robust estimates of class membership to be estimated using a technique known as boosting. This approach allows poorly separable classes to be distinguished based on ancillary information, but does not penalize rare classes.