Randomized SVD Methods in Hyperspectral Imaging
We present a randomized singular value decomposition (rSVD) method for the purposes of lossless compression, reconstruction, classification, and target detection with hyperspectral (HSI) data. Recent work in low-rank matrix approximations obtained from random projections suggests that these approxim...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2012/409357 |
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| _version_ | 1832553966501625856 |
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| author | Jiani Zhang Jennifer Erway Xiaofei Hu Qiang Zhang Robert Plemmons |
| author_facet | Jiani Zhang Jennifer Erway Xiaofei Hu Qiang Zhang Robert Plemmons |
| author_sort | Jiani Zhang |
| collection | DOAJ |
| description | We present a randomized singular value decomposition (rSVD) method for the purposes of lossless compression, reconstruction, classification, and target detection with hyperspectral (HSI) data. Recent work in low-rank matrix approximations obtained from random projections suggests that these approximations are well suited for randomized dimensionality reduction. Approximation errors for the rSVD are evaluated on HSI, and comparisons are made to deterministic techniques and as well as to other randomized low-rank matrix approximation methods involving compressive principal component analysis. Numerical tests on real HSI data suggest that the method is promising and is particularly effective for HSI data interrogation. |
| format | Article |
| id | doaj-art-b7755fca188a4e8da542e4e81c0af0ce |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-b7755fca188a4e8da542e4e81c0af0ce2025-02-03T05:52:50ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552012-01-01201210.1155/2012/409357409357Randomized SVD Methods in Hyperspectral ImagingJiani Zhang0Jennifer Erway1Xiaofei Hu2Qiang Zhang3Robert Plemmons4Department of Mathematics, Wake Forest University, Winston-Salem, NC 27109, USADepartment of Mathematics, Wake Forest University, Winston-Salem, NC 27109, USADepartment of Mathematics, Wake Forest University, Winston-Salem, NC 27109, USADepartment of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC 27157, USADepartments of Mathematics and Computer Science, Wake Forest University, Winston-Salem, NC 27109, USAWe present a randomized singular value decomposition (rSVD) method for the purposes of lossless compression, reconstruction, classification, and target detection with hyperspectral (HSI) data. Recent work in low-rank matrix approximations obtained from random projections suggests that these approximations are well suited for randomized dimensionality reduction. Approximation errors for the rSVD are evaluated on HSI, and comparisons are made to deterministic techniques and as well as to other randomized low-rank matrix approximation methods involving compressive principal component analysis. Numerical tests on real HSI data suggest that the method is promising and is particularly effective for HSI data interrogation.http://dx.doi.org/10.1155/2012/409357 |
| spellingShingle | Jiani Zhang Jennifer Erway Xiaofei Hu Qiang Zhang Robert Plemmons Randomized SVD Methods in Hyperspectral Imaging Journal of Electrical and Computer Engineering |
| title | Randomized SVD Methods in Hyperspectral Imaging |
| title_full | Randomized SVD Methods in Hyperspectral Imaging |
| title_fullStr | Randomized SVD Methods in Hyperspectral Imaging |
| title_full_unstemmed | Randomized SVD Methods in Hyperspectral Imaging |
| title_short | Randomized SVD Methods in Hyperspectral Imaging |
| title_sort | randomized svd methods in hyperspectral imaging |
| url | http://dx.doi.org/10.1155/2012/409357 |
| work_keys_str_mv | AT jianizhang randomizedsvdmethodsinhyperspectralimaging AT jennifererway randomizedsvdmethodsinhyperspectralimaging AT xiaofeihu randomizedsvdmethodsinhyperspectralimaging AT qiangzhang randomizedsvdmethodsinhyperspectralimaging AT robertplemmons randomizedsvdmethodsinhyperspectralimaging |