Ensemble learning is frequently used to reduce classification error. The more popular techniques draw multiple samples from the training data and employ a voting procedure to aggregate the decisions of the classifiers constructed from those samples. In practice, such ensemble methods have been shown to work well and improve accuracy. Here we present a meta-learning strategy that combines the decisions of classifiers constructed from spatial models taken at multiple resolutions. By varying the resolution from coarse to fine-grained, we are able to partition the data on global features that describe a majority of the objects, as well as small, local features that are present in just a few problem cases. We test our technique on a biomedical dataset containing surface elevation values for diseased and non-diseased corneas. We transform these elevations into a series of coefficients using two different spatial transformations. Using these coefficients, we determine how well they distinguish between the two classes. We find our algorithm can increase the classification accuracy of a single decision tree up to 10% and can also be used in conjunction with traditional meta-learning techniques such as bagging to further improve performance. In an attempt to improve the execution time of the transformation algorithms, we have developed a distributed, grid-based implementation as well.