Denoising of hyperspectral imagery using principal component analysis and wavelet shrinkage

IEEE Transactions on Geoscience and Remote Sensing

Volumn 49, Issue 3, 2011, Pages 973-980

  Chen, Guangyi ^a   Qian, Shen En ^a  

^a CANADIAN SPACE AGENCY   (Canada)

Author keywords

Denoising; Hyperspectral imagery; Principal component analysis (PCA); Wavelet shrinkage

Indexed keywords

BIVARIATE; DATA CUBE; DE-NOISING; DENOISING METHODS; DUAL-TREE COMPLEX WAVELET TRANSFORM; FINE FEATURE; HYPERSPECTRAL DATA; HYPERSPECTRAL IMAGERY; IMAGE DENOISING METHODS; IMAGE INFORMATION; LOW ENERGIES; NOISE LEVELS; OUTPUT CHANNELS; PRINCIPAL COMPONENT ANALYSIS (PCA); SIGNAL TO NOISE; TOTAL ENERGY; WAVELET SHRINKAGE; WAVELET THRESHOLDING METHOD;

GEOMETRY; METADATA; NOISE POLLUTION CONTROL; REMOTE SENSING; SHRINKAGE; SIGNAL TO NOISE RATIO; TREES (MATHEMATICS); WAVELET TRANSFORMS;

PRINCIPAL COMPONENT ANALYSIS;

ACOUSTIC DATA; IMAGE ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; SIGNAL-TO-NOISE RATIO; WAVELET;

EID: 79952041437     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2010.2075937     Document Type: Article

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