Ensemble diversity analysis on remote sensing data classification using random forests

Ensemble classifiers perform better than single classifiers and result in reduced generalisation error. Diversity across ensemble members is a key factor affecting classification performance. Here, an original exploration of the relationship between ensemble diversity and classification performance applied to large area remote sensing classification, using random forests, is undertaken. Results demonstrate how targeting lower margin training samples is both a strategy for inducing diversity in ensemble classifiers and achieving better classifier performance for difficult or rare classes, and a way to reduce data redundancy.