A comparison of random forest and Adaboost tree in ecosystem classification in east Mojave Desert

Xin Miao, Missouri State University
Jill S. Heaton


We compared two basic ensemble methods, namely random forest and Adaboost tree for the classification of ecosystems in Clark County, Nevada, USA through multitemporal multisource LANDSAT TM/ETM+ images and terrain-related GIS data layers. Random forest generates decision trees by randomly selecting a limited number features from all available features for node splitting, and each tree cast a vote for the final decision. On the other hand, Adaboost tree is an iterative approach to improve the performance of a weak classifier by assigning weights to training samples, and incorrectly classified training samples will gain a larger weight in the process. We discuss the properties of these two tree-based ensemble methods and compare their classification performances in ecosystem classification. The results show that Adaboost tree can provide higher classification accuracy than random forest in multitemporal multisource dataset, while the latter could be more efficient in computation.