Tensor Decomposition and PCA Jointed Algorithm for Hyperspectral Image Denoising

Denoising is a critical preprocessing step for hyperspectral image (HSI) classification and detection. Traditional methods usually convert high-dimensional HSI data to 2-D data and process them separately. Consequently, the inherent structured high-dimensional information in the original observation...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2016-07, Vol.13 (7), p.897-901
Main Authors: Meng, Shushu, Huang, Long-Ting, Wang, Wen-Qin
Format: Article
Language:English
Subjects:
Classification
Decomposition
Hyperspectral image (HSI)
Hyperspectral imaging
Image classification
Image detection
Mathematical analysis
Matrix decomposition
Noise measurement
noise power ratio (NPR)
Noise reduction
peak signal-to-noise ratio (PSNR)
Preprocessing
Principal component analysis
principal component analysis (PCA)
PSNR
Signal to noise ratio
Tensile stress
tensor decomposition
Tucker decomposition
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Description
Summary:Denoising is a critical preprocessing step for hyperspectral image (HSI) classification and detection. Traditional methods usually convert high-dimensional HSI data to 2-D data and process them separately. Consequently, the inherent structured high-dimensional information in the original observations may be discarded. To overcome this disadvantage, this letter tackles an HSI denoising by jointly exploiting Tucker decomposition and principal component analysis (PCA). A truncated Tucker decomposition method based on noise power ratio (NPR) analysis and jointed with PCA is presented. We call this jointed method as NPR-Tucker+PCA. Experimental results show that the proposed method outperforms existing methods in the sense of peak signal-to-noise ratio performance.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2016.2552403