Experimental comparison of support vector machines with random forests for hyperspectral image land cover classiﬁcation
Abe, B. T.
Olugbara, Oludayo O.
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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.
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