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Proceedings Paper

Spectral decorrelation of hyperspectral imagery using fractional wavelet transform
Author(s): B. Uğur Töreyin
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Paper 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.

Paper Details

Date Published: 9 June 2016
PDF: 4 pages
Proc. SPIE 9874, Remotely Sensed Data Compression, Communications, and Processing XII, 98740B (9 June 2016); doi: 10.1117/12.2224579
Show Author Affiliations
B. Uğur Töreyin, Istanbul Technical Univ. (Turkey)


Published in SPIE Proceedings Vol. 9874:
Remotely Sensed Data Compression, Communications, and Processing XII
Bormin Huang; Chein-I Chang; Chulhee Lee, Editor(s)

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