Random forest classifier for remote sensing classification
Top Cited Papers
- 1 January 2005
- journal article
- research article
- Published by Informa UK Limited in International Journal of Remote Sensing
- Vol. 26 (1), 217-222
- https://doi.org/10.1080/01431160412331269698
Abstract
Growing an ensemble of decision trees and allowing them to vote for the most popular class produced a significant increase in classification accuracy for land cover classification. The objective of this study is to present results obtained with the random forest classifier and to compare its performance with the support vector machines (SVMs) in terms of classification accuracy, training time and user defined parameters. Landsat Enhanced Thematic Mapper Plus (ETM+) data of an area in the UK with seven different land covers were used. Results from this study suggest that the random forest classifier performs equally well to SVMs in terms of classification accuracy and training time. This study also concludes that the number of user‐defined parameters required by random forest classifiers is less than the number required for SVMs and easier to define.Keywords
This publication has 9 references indexed in Scilit:
- LIBSVMACM Transactions on Intelligent Systems and Technology, 2011
- SmcHD1, containing a structural-maintenance-of-chromosomes hinge domain, has a critical role in X inactivationNature Genetics, 2008
- An assessment of the effectiveness of decision tree methods for land cover classificationRemote Sensing of Environment, 2003
- Multiple classifiers applied to multisource remote sensing dataIEEE Transactions on Geoscience and Remote Sensing, 2002
- Application of the MODIS global supervised classification model to vegetation and land cover mapping of Central AmericaInternational Journal of Remote Sensing, 2000
- Maximizing land cover classification accuracies produced by decision trees at continental to global scalesIEEE Transactions on Geoscience and Remote Sensing, 1999
- The Nature of Statistical Learning TheoryPublished by Springer Science and Business Media LLC ,1995
- Constructing Decision TreesPublished by Elsevier BV ,1993
- An Empirical Comparison of Pruning Methods for Decision Tree InductionMachine Learning, 1989