Combining Multiple Classifiers: An Application Using Spatial and Remotely Sensed Information for Land Cover Type Mapping

This article discusses two new methods for increasing the accuracy of classifiers used land cover mapping. The first method, called the product rule, is a simple and general method of combining two or more classification rules as a single rule. Stacked regression methods of combining classification rules are discussed and compared to the product rule. The second method of increasing classifier accuracy is a simple nonparametric classifier that uses spatial information for classification. Two data sets used for land cover mapping of Landsat TM scenes from Idaho and Montana illustrate the new methods. For these examples, the product rule compared favorably to the more complex stacked regression methods. The spatial classifier produced substantial increases in estimated accuracy when combined with one or more additional classifiers that used remotely sensed variables for classification. These results suggest that the product rule may produce increases in map accuracy with little additional expense or effort. The spatial classifier may be useful for increasing accuracy when patterns exist in the spatial distribution of land cover.