Hyperspectral Image Denoising via Noise-Adjusted Iterative Low-Rank Matrix Approximation

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volumn 8, Issue 6, 2015, Pages 3050-3061

  He, Wei ^a   Zhang, Hongyan ^a   Zhang, Liangpei ^a   Shen, Huanfeng ^b  

^a Wuhan University   (China)

^b WUHAN UNIVERSITY   (China)

Author keywords

Denoising; hyperspectral image (HSI); low rank matrix approximation (LRMA); noise adjusted iteration; Randomized singular value decomposition (RSVD)

Indexed keywords

INDEPENDENT COMPONENT ANALYSIS; ITERATIVE METHODS; MATRIX ALGEBRA; OPTIMIZATION; SIGNAL TO NOISE RATIO; SINGULAR VALUE DECOMPOSITION; SPECTROSCOPY;

DE-NOISING; HYPERSPECTRAL IMAGES; LOW-RANK MATRIX APPROXIMATIONS; NOISE-ADJUSTED ITERATION; RANDOMIZED SINGULAR VALUE DECOMPOSITION (RSVD);

IMAGE DENOISING;

COMPUTER SIMULATION; DATA PROCESSING; DECOMPOSITION ANALYSIS; IMAGE ANALYSIS; MATRIX; OPTIMIZATION; SPECTRAL ANALYSIS;

EID: 85027932384     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2015.2398433     Document Type: Article

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