Spectral decorrelation of hyperspectral imagery using fractional wavelet transform

B. Uğur Töreyin

doi:10.1117/12.2224579

9 June 2016 Spectral decorrelation of hyperspectral imagery using fractional wavelet transform

B. Uğur Töreyin

Proceedings Volume 9874, Remotely Sensed Data Compression, Communications, and Processing XII; 98740B (2016) https://doi.org/10.1117/12.2224579
Event: SPIE Commercial + Scientific Sensing and Imaging, 2016, Baltimore, MD, United States

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.

Citation Download Citation

B. Uğur Töreyin "Spectral decorrelation of hyperspectral imagery using fractional wavelet transform", Proc. SPIE 9874, Remotely Sensed Data Compression, Communications, and Processing XII, 98740B (9 June 2016); https://doi.org/10.1117/12.2224579

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available