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Imported fire ants construct earthen nests (mounds) that exhibit many characteristics which make them potentially good targets for remote sensing programs, including geographical orientation, topography, and bare soil surrounded by actively growing vegetation. Template-based features and object-based features extracted from aerial multispectral imagery of fire ant infested pastures were used to construct classifiers for automated fire ant mound detection. A classifier constructed using template-based features alone yielded a 79% probability of detection with a corresponding false positive rate of 9%. Addition of object-based features (compactness and symmetry) to the classifier yielded a 79% probability of detection with a corresponding false positive rate of 4%. Maintaining a 79% detection rate when applying the classifier to a second, unique pasture dataset with different seasonal and other environmental factors resulted in a false positive rate of 17.5%. Data demonstrate that automated detection of mounds with classifiers incorporating template- and object-based features is feasible, but it may be necessary to construct unique classifiers on a site-specific basis.