Spectral decorrelation of hyperspectral imagery using fractional wavelet transform

B. Uür Töreyin

doi:10.1117/12.2224579

Spectral decorrelation of hyperspectral imagery using fractional wavelet transform

B. Uür Töreyin^*

^*Corresponding author for this work

Informatics Institute

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Hyperspectral data is composed of a set of correlated band images. In order to efficiently compress the hyperspectral imagery, this inherent correlation may be exploited by means of spectral decorrelators. In this paper, a fractional wavelet transform based method is introduced for spectral decorrelation of hyperspectral data. As opposed to regular wavelet transform which decomposes a given signal into two equal-length sub-signals, fractional wavelet transform is carried out by decomposing the signal corresponding to the spectral content into two sub-signals with different lengths. Sub-signal lengths are adapted to data to achieve a better spectral decorrelation. Performance results pertaining to AVIRIS datasets are presented in comparison with existing regular wavelet decomposition based compression methods.

Original language	English
Title of host publication	Remotely Sensed Data Compression, Communications, and Processing XII
Editors	Chulhee Lee, Bormin Huang, Chein-I Chang
Publisher	SPIE
ISBN (Electronic)	9781510601154
DOIs	https://doi.org/10.1117/12.2224579
Publication status	Published - 2016
Event	Remotely Sensed Data Compression, Communications, and Processing XII - Baltimore, United States Duration: 20 Apr 2016 → 21 Apr 2016

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	9874
ISSN (Print)	0277-786X
ISSN (Electronic)	1996-756X

Conference

Conference	Remotely Sensed Data Compression, Communications, and Processing XII
Country/Territory	United States
City	Baltimore
Period	20/04/16 → 21/04/16

Bibliographical note

Publisher Copyright:
© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

Keywords

fracti onal wavelet transform
hyperspectral image compression
hyperspectral imagery
spectral decorrelation

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10.1117/12.2224579

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title = "Spectral decorrelation of hyperspectral imagery using fractional wavelet transform",

abstract = "Hyperspectral data is composed of a set of correlated band images. In order to efficiently compress the hyperspectral imagery, this inherent correlation may be exploited by means of spectral decorrelators. In this paper, a fractional wavelet transform based method is introduced for spectral decorrelation of hyperspectral data. As opposed to regular wavelet transform which decomposes a given signal into two equal-length sub-signals, fractional wavelet transform is carried out by decomposing the signal corresponding to the spectral content into two sub-signals with different lengths. Sub-signal lengths are adapted to data to achieve a better spectral decorrelation. Performance results pertaining to AVIRIS datasets are presented in comparison with existing regular wavelet decomposition based compression methods.",

keywords = "fracti onal wavelet transform, hyperspectral image compression, hyperspectral imagery, spectral decorrelation",

author = "T{\"o}reyin, {B. U{\"u}r}",

note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; Remotely Sensed Data Compression, Communications, and Processing XII ; Conference date: 20-04-2016 Through 21-04-2016",

year = "2016",

doi = "10.1117/12.2224579",

language = "English",

series = "Proceedings of SPIE - The International Society for Optical Engineering",

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editor = "Chulhee Lee and Bormin Huang and Chein-I Chang",

booktitle = "Remotely Sensed Data Compression, Communications, and Processing XII",

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}

Töreyin, BU 2016, Spectral decorrelation of hyperspectral imagery using fractional wavelet transform. in C Lee, B Huang & C-I Chang (eds), Remotely Sensed Data Compression, Communications, and Processing XII., 98740B, Proceedings of SPIE - The International Society for Optical Engineering, vol. 9874, SPIE, Remotely Sensed Data Compression, Communications, and Processing XII, Baltimore, United States, 20/04/16. https://doi.org/10.1117/12.2224579

Spectral decorrelation of hyperspectral imagery using fractional wavelet transform. / Töreyin, B. Uür.
Remotely Sensed Data Compression, Communications, and Processing XII. ed. / Chulhee Lee; Bormin Huang; Chein-I Chang. SPIE, 2016. 98740B (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 9874).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Spectral decorrelation of hyperspectral imagery using fractional wavelet transform

AU - Töreyin, B. Uür

N1 - Publisher Copyright: © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

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Y1 - 2016

N2 - Hyperspectral data is composed of a set of correlated band images. In order to efficiently compress the hyperspectral imagery, this inherent correlation may be exploited by means of spectral decorrelators. In this paper, a fractional wavelet transform based method is introduced for spectral decorrelation of hyperspectral data. As opposed to regular wavelet transform which decomposes a given signal into two equal-length sub-signals, fractional wavelet transform is carried out by decomposing the signal corresponding to the spectral content into two sub-signals with different lengths. Sub-signal lengths are adapted to data to achieve a better spectral decorrelation. Performance results pertaining to AVIRIS datasets are presented in comparison with existing regular wavelet decomposition based compression methods.

AB - Hyperspectral data is composed of a set of correlated band images. In order to efficiently compress the hyperspectral imagery, this inherent correlation may be exploited by means of spectral decorrelators. In this paper, a fractional wavelet transform based method is introduced for spectral decorrelation of hyperspectral data. As opposed to regular wavelet transform which decomposes a given signal into two equal-length sub-signals, fractional wavelet transform is carried out by decomposing the signal corresponding to the spectral content into two sub-signals with different lengths. Sub-signal lengths are adapted to data to achieve a better spectral decorrelation. Performance results pertaining to AVIRIS datasets are presented in comparison with existing regular wavelet decomposition based compression methods.

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KW - hyperspectral image compression

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A2 - Lee, Chulhee

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Spectral decorrelation of hyperspectral imagery using fractional wavelet transform

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