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Im, Jungho
Intelligent Remote sensing and geospatial Information Science (IRIS) Lab
Research Interests
  • Remote sensing, Geospatial modeling, Disaster monitoring and management, Climate change

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Support vector machines in remote sensing: A review

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Title
Support vector machines in remote sensing: A review
Author
Mountrakis, GiorgosIm, JunghoOgole, Caesar
Keywords
Remote sensing; Review; Support vector machines; SVM; SVMs
Issue Date
2011-05
Publisher
ELSEVIER SCIENCE BV
Citation
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, v.66, no.3, pp.247 - 259
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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DOI
10.1016/j.isprsjprs.2010.11.001
ISSN
0924-2716
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UEE_Journal Papers
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