Compression of Hyperspectral Images Using Discerete Wavelet Transform and Tucker Decomposition

The compression of hyperspectral images (HSIs) has recently become a very attractive issue for remote sensing applications because of their volumetric data. In this paper, an efficient method for hyperspectral image compression is presented. The proposed algorithm, based on Discrete Wavelet Transfor...

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Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing 2012-04, Vol.5 (2), p.444-450
Main Authors: Karami, Azam, Yazdi, Mehran, Mercier, Grégoire
Format: Article
Language:English
Subjects:
Compression
Correlation
Discrete wavelet transforms
Encoding
Engineering Sciences
hyperspectral images
Hyperspectral imaging
Image coding
noise reduction
Remote sensing
Signal and Image processing
Studies
Tensile stress
tucker decomposition
wavelet transform
Wavelet transforms
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Summary:The compression of hyperspectral images (HSIs) has recently become a very attractive issue for remote sensing applications because of their volumetric data. In this paper, an efficient method for hyperspectral image compression is presented. The proposed algorithm, based on Discrete Wavelet Transform and Tucker Decomposition (DWT-TD), exploits both the spectral and the spatial information in the images. The core idea behind our proposed technique is to apply TD on the DWT coefficients of spectral bands of HSIs. We use DWT to effectively separate HSIs into different sub-images and TD to efficiently compact the energy of sub-images. We evaluate the effect of the proposed method on real HSIs and also compare the results with the well-known compression methods. The obtained results show a better performance of the proposed method. Moreover, we show the impact of compression HSIs on the supervised classification and linear unmixing.
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2012.2189200