Support Vector Machine-Based Endmember Extraction
Introduced in this paper is the utilization of support vector machines (SVMs) to semiautomatically perform endmember extraction from hyperspectral data. The strengths of SVM are exploited to provide a fast and accurate calculated representation of high-dimensional data sets that may consist of multi...
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| Veröffentlicht in: | IEEE transactions on geoscience and remote sensing 2009-03, Vol.47 (3), p.771-791 |
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| container_title | IEEE transactions on geoscience and remote sensing |
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| creator | Filippi, A.M. Archibald, R. |
| description | Introduced in this paper is the utilization of support vector machines (SVMs) to semiautomatically perform endmember extraction from hyperspectral data. The strengths of SVM are exploited to provide a fast and accurate calculated representation of high-dimensional data sets that may consist of multiple distributions. Once this representation is computed, the number of distributions can be determined without prior knowledge. For each distribution, an optimal transform can be determined that preserves informational content while reducing the data dimensionality and, hence, the computational cost. Finally, endmember extraction for the whole data set is accomplished. Results indicate that this SVM-based endmember extraction algorithm has the capability of semiautonomously determining endmembers from multiple clusters with computational speed and accuracy while maintaining a robust tolerance to noise. |
| doi_str_mv | 10.1109/TGRS.2008.2004708 |
| format | Article |
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(ORNL), Oak Ridge, TN (United States)</creatorcontrib><creatorcontrib>Center for Computational Sciences</creatorcontrib><title>Support Vector Machine-Based Endmember Extraction</title><title>IEEE transactions on geoscience and remote sensing</title><addtitle>TGRS</addtitle><description>Introduced in this paper is the utilization of support vector machines (SVMs) to semiautomatically perform endmember extraction from hyperspectral data. The strengths of SVM are exploited to provide a fast and accurate calculated representation of high-dimensional data sets that may consist of multiple distributions. Once this representation is computed, the number of distributions can be determined without prior knowledge. For each distribution, an optimal transform can be determined that preserves informational content while reducing the data dimensionality and, hence, the computational cost. Finally, endmember extraction for the whole data set is accomplished. 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(ORNL), Oak Ridge, TN (United States)</aucorp><aucorp>Center for Computational Sciences</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Support Vector Machine-Based Endmember Extraction</atitle><jtitle>IEEE transactions on geoscience and remote sensing</jtitle><stitle>TGRS</stitle><date>2009-03-01</date><risdate>2009</risdate><volume>47</volume><issue>3</issue><spage>771</spage><epage>791</epage><pages>771-791</pages><issn>0196-2892</issn><issn>0034-4257</issn><eissn>1558-0644</eissn><eissn>1879-0704</eissn><coden>IGRSD2</coden><abstract>Introduced in this paper is the utilization of support vector machines (SVMs) to semiautomatically perform endmember extraction from hyperspectral data. The strengths of SVM are exploited to provide a fast and accurate calculated representation of high-dimensional data sets that may consist of multiple distributions. 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| identifier | ISSN: 0196-2892 |
| ispartof | IEEE transactions on geoscience and remote sensing, 2009-03, Vol.47 (3), p.771-791 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | ACCURACY ALGORITHMS Applied geophysics Computation Computational efficiency COMPUTERS Data mining DATA PROCESSING DISTRIBUTION Earth sciences Earth, ocean, space Endmember extraction Exact sciences and technology Extraction Humans Hyperspectral imaging Hyperspectral sensors Indexes Internal geophysics Laboratories Mathematical analysis MATHEMATICAL METHODS AND COMPUTING Optical noise Pixel Preserves Remote sensing Representations Support vector machines support vector machines (SVMs) TOLERANCE VECTORS |
| title | Support Vector Machine-Based Endmember Extraction |
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