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Volumn , Issue , 2014, Pages 1536-1539

A noise-adjusted iterative randomized singular value decomposition method for hyperspectral image denoising

Author keywords

denoising; hyperspectral image; low rank; NAIRSVD; PCA

Indexed keywords

HYPERSPECTRAL IMAGING; INDEPENDENT COMPONENT ANALYSIS; ITERATIVE METHODS; PRINCIPAL COMPONENT ANALYSIS; REMOTE SENSING; SINGULAR VALUE DECOMPOSITION; SPECTROSCOPY;

EID: 84911456602     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IGARSS.2014.6946731     Document Type: Conference Paper
Times cited : (10)

References (11)
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  • 3
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    • Zhang, H.1
  • 5
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    • A. A. Green, M. Berman, P. Switzer, and M. D. Craig, "A transformation for ordering multispectral data in terms of image quality with implications for noise removal," IEEE Trans. Geosci. Remote Sens., Vol. 26, pp. 65-74, Jan. 1988.
    • (1988) IEEE Trans. Geosci. Remote Sens. , vol.26 , pp. 65-74
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    • Hyperspectral image restoration using low-rank matrix recovery
    • to be published
    • H. Zhang, W. He, L. Zhang, H. Shen, and Q. Yuan, "Hyperspectral Image Restoration Using Low-Rank Matrix Recovery," IEEE Trans. Geosci. Remote Sens., DOI: 10.1109/TGRS.2013.2284280, to be published.
    • IEEE Trans. Geosci. Remote Sens.
    • Zhang, H.1    He, W.2    Zhang, L.3    Shen, H.4    Yuan, Q.5
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    • Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.