Journal
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
Volume 66, Issue 3, Pages 247-259Publisher
ELSEVIER
DOI: 10.1016/j.isprsjprs.2010.11.001
Keywords
Support vector machines; Review; Remote sensing; SVM; SVMs
Categories
Funding
- National Science Foundation [GRS-0648393]
- National Aeronautics and Space Administration [NNX08AR11G, NNX09AK16G]
- Syracuse Center of Excellence CARTI Program
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A wide range of methods for analysis or 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. (C) 2010 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
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