An evaluation of bagging, boosting, and random forests for land-cover classification in Cape Cod, Massachusetts, USA

Ghimire, Bardan, Rogan, John, Rodríguez Galiano, Víctor, Panday, Prajjwal and Neeti, Neeti (2012) An evaluation of bagging, boosting, and random forests for land-cover classification in Cape Cod, Massachusetts, USA. GIScience & Remote Sensing, 49 (5), 623-643. (doi:10.2747/1548-1603.49.5.623).

Abstract

The iterative and convergent nature of ensemble learning algorithms provides potential for improving classification of complex landscapes. This study performs land-cover classification in a heterogeneous Massachusetts landscape by comparing three ensemble learning techniques (bagging, boosting, and random forests) and a non-ensemble learning algorithm (classification trees) using multiple criteria related to algorithm and training data characteristics. The ensemble learning algorithms had comparably high accuracy (Kappa range: 0.76-0.78), which was 11% higher than that of classification trees. Ensemble learning techniques were not influenced by calibration data variability, were robust to one-fifth calibration data noise, and insensitive to a 50% reduction in calibration data size.

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