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 Vol. 13; no. 7; pp. 897 - 901
Main Authors:	Meng, Shushu, Huang, Long-Ting, Wang, Wen-Qin
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01-07-2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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 Hyperspectral image (HSI) Tucker decomposition noise power ratio (NPR) peak signal-to-noise ratio (PSNR) principal component analysis (PCA) tensor decomposition
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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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1545-598X 1558-0571
DOI:	10.1109/LGRS.2016.2552403