Simultaneous dimensionality reduction and denoising of hyperspectral imagery using bivariate wavelet shrinking and principal component analysis

In this paper, we propose a method that not only reduces the dimensionality of a hyperspectral data cube but also removes noise in the data cube by combining the bivariate wavelet thresholding with principal component analysis (PCA). The data cube thus obtained is applied to mineral endmember extrac...

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Bibliographic Details
Published in:Canadian journal of remote sensing 2008-10, Vol.34 (5), p.447-454
Main Authors: Chen, Guangyi, Qian, Shen-En
Format: Article
Language:English
Subjects:
Channels
Cubes
CUPRITE
Data analysis
Data processing
Extraction
Extraction processes
EXTRACTIVE METALLURGY
MINERALS
NOISE
Principal component analysis
Principal components analysis
Reduction
Remote sensing
Wavelet
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Summary:In this paper, we propose a method that not only reduces the dimensionality of a hyperspectral data cube but also removes noise in the data cube by combining the bivariate wavelet thresholding with principal component analysis (PCA). The data cube thus obtained is applied to mineral endmember extraction and mineral detection. The reason why we incorporate bivariate wavelet denoising into PCA dimensionality reduction is because the dimensionality-reduced channels using PCA often contain significant amounts of noise. By reducing noise in the data cube, we can get better dimensionality-reduced output channels for hyperspectral data analysis and processing. Experiments reported in this paper confirm that the proposed method outperforms PCA for endmember extraction and mineral detection using the Cuprite data cube.
ISSN:0703-8992
1712-7971
DOI:10.5589/m08-058