Please use this identifier to cite or link to this item: https://hdl.handle.net/10321/1137
Title: Experimental comparison of support vector machines with random forests for hyperspectral image land cover classification
Authors: Marwala, T. 
Abe, B. T. 
Olugbara, Oludayo O. 
Keywords: Hyperspectral image;Land cover;Generalized reduced gradient;Classifiers
Issue Date: 12-Jun-2014
Publisher: Indian Academy of Sciences
Source: Abe, B.T.; Olugbara, O.O. and Marwala, T. 2014. Experimental comparison of support vector machines with random forests for hyperspectral image land cover classification. Journal of Earth System Science. 123 (4): 779-790
Journal: Journal of earth system science (Online) 
Abstract: 
The performances of regular support vector machines and random forests are experimentally com-pared for hyperspectral imaging land cover classification. Special characteristics of hyperspectral imaging dataset present diverse processing problems to be resolved under robust mathematical formalisms such as image classification. As a result, pixel purity index algorithm is used to obtain endmember spectral responses from Indiana pine hyperspectral image dataset. The generalized reduced gradient optimiza-tion algorithm is thereafter executed on the research data to estimate fractional abundances in the hyperspectral image and thereby obtain the numeric values for land cover classification. The Waikato environment for knowledge analysis (WEKA) data mining framework is selected as a tool to carry out the classification process by using support vector machines and random forests classifiers. Results show that performance of support vector machines is comparable to that of random forests. This study makes a positive contribution to the problem of land cover classification by exploring generalized reduced gra-dient method, support vector machines, and random forests to improve producer accuracy and overall classification accuracy. The performance comparison of these classifiers is valuable for a decision maker to consider tradeoffs in method accuracy versus method complexity.
URI: http://hdl.handle.net/10321/1137
ISSN: 0253-4126
0973-774X
DOI: 10.1007/s12040-014-0436-x
Appears in Collections:Research Publications (Accounting and Informatics)

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