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9 June 2016 Spectral decorrelation of hyperspectral imagery using fractional wavelet transform
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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.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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);

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