Fast Compression Algorithm for Hyperspectral Image Based on Dispersion Sorting in Transform Domain

CHEN Shanxue; ZHENG Wenjing; ZHANG Jiajia; LI Fangwei

doi:10.13203/j.whugis20140270

Volume 41 Issue 7

Jul. 2016

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Geomatics and Information Science of Wuhan University > 2016 > 41(7): 868-874. > DOI: 10.13203/j.whugis20140270

CHEN Shanxue, ZHENG Wenjing, ZHANG Jiajia, LI Fangwei. Fast Compression Algorithm for Hyperspectral Image Based on Dispersion Sorting in Transform Domain[J]. Geomatics and Information Science of Wuhan University, 2016, 41(7): 868-874. DOI: 10.13203/j.whugis20140270

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Fast Compression Algorithm for Hyperspectral Image Based on Dispersion Sorting in Transform Domain

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Chongqing Key Laboratary of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

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Received Date: February 17, 2015
Published Date: July 04, 2016

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A fast compression algorithm for hyperspectral images based on dispersion sorting in transform domain is proposedConsidering the characteristics of hyperspectral data in the Hadamard domain, the proposed algorithm selects a favourable order adaptively and sorts the dimensions of spectral vectors by dispersion. Consequently, the energy and difference of the spectral vectors is concentrated on the lower dimensions and the dimensions of high signal to noise ratio are moved into low dimensional subspace. Then, efficient eliminating inequalities are constructed. When combinined with the LBG(Linde Bazo Gray) clustering algorithm, the proposed algorithm quickly completes the encoding of hyperspectral images via vector quantization. Experiments were conducted under different compression ratios The results show that, the compression algorithm for hyperspectral images as presented in this paper can reduce the computational complexity significantly when completing fast compression based on the precondition of good recovery quality.
- hyperspectral image,
- image compression,
- dispersion sorting,
- Hadamard transform,
- vector quantization

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