Ensemble of support vector machines for land cover classification

Author: Pal, M.

Source: International Journal of Remote Sensing, Volume 29, Number 10, 2008 , pp. 3043-3049(7)

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Abstract:

This letter presents the results of two different ensemble approaches to increase the accuracy of land cover classification using support vector machines. Finite ensemble approaches, based on boosting and bagging and infinite ensemble created by embedding the infinite hypothesis in the kernel of support vector machines, are discussed. Results suggest that the infinite ensemble approach provides a significant increase in the classification accuracy in comparison to the radial basis function kernel-based support vector machines. While using finite ensemble approaches, bagging works well and provides a comparable performance to the infinite ensemble approach, whereas boosting decreases the performance of support vector machines. Comparison in terms of computational cost suggests that finite ensemble approaches require a large processing time in comparison to the infinite ensemble approach.

Document Type: Research article

DOI: http://dx.doi.org/10.1080/01431160802007624

Affiliations: 1: Department of Civil Engineering, National Institute of Technology, Kurukshetra, 136119 Haryana, India

Publication date: 2008-01-01

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