Spectral Decorrelation of Hyperspectral Imagery Using Fractional Wavelet Transform


Töreyin B. U.

Conference on Remotely Sensed Data Compression, Communications, and Processing XII, Maryland, United States Of America, 20 - 21 April 2016, vol.9874 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 9874
  • Doi Number: 10.1117/12.2224579
  • City: Maryland
  • Country: United States Of America

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.