Randomized SVD Methods in Hyperspectral Imaging
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 01, 2012
- Source ID
- 10.1155/2012/409357
Entities
People
- Jennifer Erway
- Jiani Zhang
- Qiang Zhang
- Robert Plemmons
- Xiaofei Hu
Organizations
- National Geospatial-Intelligence Agency
- Wake Forest School of Medicine
- Wake Forest University