Random forest classification of multisource remote sensing and geographic data

The use of random forests for classification of multisource data is investigated in this paper. Random Forest is a classifier that grows many classification trees. Each tree is trained on a bootstrapped sample of the training data, and at each node the algorithm only searches across a random subset of the variables to determine a split. To classify an input vector in random forest, the vector is submitted as an input to each of the trees in the forest, and the classification is then determined by a majority vote. The experiments presented in the paper were done on a multisource remote sensing and geographic data set. The experimental results obtained with random forests were compared to results obtained by bagging and boosting methods.