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.

Keywords

Boosting (machine learning)Random forestEnsemble learningLand coverMachine learningArtificial intelligenceCalibrationEnsemble forecastingComputer scienceStatistical classificationGradient boostingAdaBoostRemote sensingGeographyMathematicsLand useStatisticsSupport vector machineEngineering

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Publication Info

Year
2012
Type
article
Volume
49
Issue
5
Pages
623-643
Citations
233
Access
Closed

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Cite This

Bardan Ghimire, John Rogan, Víctor Rodríguez‐Galiano et al. (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. https://doi.org/10.2747/1548-1603.49.5.623

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DOI
10.2747/1548-1603.49.5.623