Support vector machines in remote sensing: A review |
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| Authors: | Giorgos Mountrakis Jungho Im Caesar Ogole |
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| Institution: | a Department of Environmental Resources Engineering, SUNY College of Environmental Science and Forestry, 1 Forestry Dr, Syracuse, NY 13210, USA |
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| Abstract: | 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 common limitation for remote sensing applications. However, they also suffer from parameter assignment issues that can significantly affect obtained results. A summary of empirical results is provided for various applications of over one hundred published works (as of April, 2010). It is our hope that this survey will provide guidelines for future applications of SVMs and possible areas of algorithm enhancement. |
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| Keywords: | Support vector machines Review Remote sensing SVM SVMs |
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