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IGARSS
2009

Classification Performance of Random-projection-based Dimensionality Reduction of Hyperspectral Imagery

9 years 5 months ago
Classification Performance of Random-projection-based Dimensionality Reduction of Hyperspectral Imagery
High-dimensional data such as hyperspectral imagery is traditionally acquired in full dimensionality before being reduced in dimension prior to processing. Conventional dimensionality reduction on-board remote devices is often prohibitive due to limited computational resources; on the other hand, integrating random projections directly into signal acquisition offers alternative dimensionality reduction without sender-side computational cost. Effective receiver-side reconstruction from such random projections has been demonstrated previously using compressive-projection principal component analysis (CPPCA). While this prior work has focused on squared-error quality measures, the present work reports experimental results illustrating preservation of statistical class separation and anomaly-detection performance for CPPCA reconstruction following random-projection-based dimensionality reduction.
James E. Fowler, Qian Du, Wei Zhu, Nicolas H. Youn
Added 20 Feb 2011
Updated 20 Feb 2011
Type Journal
Year 2009
Where IGARSS
Authors James E. Fowler, Qian Du, Wei Zhu, Nicolas H. Younan
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