Denoising and Dimensionality Reduction Using Multilinear Tools for Hyperspectral Images

In hyperspectral image (HSI) analysis, classification requires spectral dimensionality reduction (DR). While common DR methods use linear algebra, we propose a multilinear algebra method to jointly achieve denoising reduction and DR. Multilinear tools consider HSI data as a whole by processing joint...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2008-04, Vol.5 (2), p.138-142
Main Authors: Renard, N., Bourennane, S., Blanc-Talon, J.
Format: Article
Language:English
Subjects:
Approximation
Approximation algorithms
Classification
Classification algorithms
Color
Computer Science
dimensionality reduction (DR)
Direct reduction
Engineering Sciences
hypercubes
Hyperspectral imaging
Image analysis
image processing
image restoration
Information Retrieval
Linear algebra
Mathematical analysis
multilinear algebra
Noise reduction
Principal component analysis
Reduction
Signal and Image processing
Spectra
Spectral analysis
Studies
Tensile stress
Wavelet transforms
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Summary:In hyperspectral image (HSI) analysis, classification requires spectral dimensionality reduction (DR). While common DR methods use linear algebra, we propose a multilinear algebra method to jointly achieve denoising reduction and DR. Multilinear tools consider HSI data as a whole by processing jointly spatial and spectral ways. The lower rank-(K 1 , K 2 , K 3 ) tensor approximation [LRTA-(K 1 , K 2 , K 3 )] was successfully applied to denoise multiway data such as color images. First, we demonstrate that the LRTA-(K 1 , K 2 , K 3 ) performs well as a denoising preprocessing to improve classification results. Then, we propose a novel method, referred to as LRTA dr -(K 1 , K 2 , D 3 ), which performs both spatial lower rank approximation and spectral DR. The classification algorithm Spectral Angle Mapper is applied to the output of the following three DR and noise reduction methods to compare their efficiency: the proposed LRTA dr -(K 1 , K 2 , D 3 ), PCA dr , and PCA dr associated with Wiener filtering or soft shrinkage of wavelet transform coefficients.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2008.915736