The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Random forest classifier for remote sensing classification
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.
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Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
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Metadata
- Authors
- Pal, M.
- Venue
- International Journal of Remote Sensing
- Publisher
- Informa UK Limited
- Published
- 2005-01
- Volume
- 26
- Issue
- 1
- Pages
- 217-222
- ISSN
- 0143-1161 (ISSN-L)
- 1366-5901
- OpenAlex
- W2155632266
- Cited by
- 3,283
- References
- 4
- FWCI
- 11.82
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
