MARGIN-BASED RANDOM FOREST FOR IMBALANCED LAND COVER CLASSIFICATION

The problem of class imbalance is often encountered in remote sensing data and has a negative effect on the classification performance of supervised classifiers even in ensemble models. The ensemble margin is a fundamental concept in ensemble learning with potential effectiveness in improving the classification of remote sensing data. This paper proposes a novel margin based extended random forest algorithm to address the class imbalance issues in the difficult context of remote sensing classification. This algorithm combines ensemble learning with data sampling. A comparative analysis is conducted with respect to standard random forest, under-sampling and over-sampling combined ensembles.