Land-cover classification from multiple classifiers using decision fusion based on the probabilistic graphical model

A novel method of using different classification algorithms in an integrated manner by adaptively weighted decision level fusion was proposed. The proposed fusion scheme involves two steps. First, we processed the data using each classifier separately and provided probability estimations for each pixel of the considered classes. Then, the results are aggregated on the basis of the decision rule of probabilistic graphical model according to the capabilities of classifiers and ancillary information. The method was tested and validated through the Landsat 8 operational land imager data using two different classifiers, namely, maximum likelihood classifier and support vector machine. The proposed method provided higher accuracy improvement than the separate use of different classifiers and that complex landscapes, such as mountainous regions, have higher accuracy improvement than the relatively homogenous ones. Moreover, the method can handle more than two types of classifiers and effectively introduce additional ancillary information for adaptive weight selection. These findings can help promote our proposed method as an emerging approach for land-cover classification through remote sensing technology.