Randomized SVD methods in hyperspectral imaging
type: Item
instance of: scholarly article
title: Randomized SVD methods in hyperspectral imaging
zbMATH Open document ID: 1253.68120
DOI: 10.1155/2012/409357
author: Jiani Zhang
author: X. F. Hu
author: Qiang Zhang
author: Jennifer B. Erway
author: Robert J. Plemmons
published in: Journal of Electrical and Computer Engineering
publication date: 2012-12-11T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 68P30
Mathematics Subject Classification ID: 62H25
zbMATH DE Number: 6114813
zbMATH Keywords: hyperspectral data
zbMATH Keywords: compression
zbMATH Keywords: reconstruction
zbMATH Keywords: classification
zbMATH Keywords: target detection
zbMATH Keywords: randomized singular value decomposition
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/409357
OpenAlex ID: W1964542748
Wikidata QID: Q58908543
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cites work: Q5689624
cites work: Principal component analysis.
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cites work: Q4337625
cites work: Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
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description: scientific article; zbMATH DE number 6114813