Use of multiple classifiers in classification of data from multiple data sources

Previously, multiple classifiers, which base their decision on the output from more than one classifier, have become popular. In this paper, the use of multiple classifiers in data fusion of multisource remote sensing and geographic data is studied. In particular, the paper focuses on the previously proposed methodologies of bagging and boosting. Bagging, boosting, and several versions of optimized statistical consensus theory are compared in classification of a multisource remote sensing and geographic data set. The results show boosting to outperform all the other methods in terms of test accuracies