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A wide range of methods for analysis of airborne-and satellite-derived imagery continues to be proposed and assessed. In this paper, we review remote sensing implementations of support vector machines (SVMs), a promising machine learning methodology. This review is timely due to the exponentially increasing number of works published in recent years. SVMs are particularly appealing in the remote sensing field due to their ability to generalize well even with limited training samples, a commondoi:10.1016/j.isprsjprs.2010.11.001 fatcat:6hx57jxaxvfxvjoqqmhk5puhty