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Proceedings Paper

Decorrelate hyperspectral images using spectral correlation
Author(s): Liang Chen; Daizhi Liu; Shiqi Huang
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Paper Abstract

This paper proposes a new algorithm for lossless compression of hyperspectral images. In our work we found hyperspectral data have unique characteristic based on spectral context and adjacent pixel spectral vectors (curves) highly correlate with each other. Pearson correlation coefficient is an effective measure of spectral similarity between spectral curves to detect horizontal and vertical spectral edge. Thus, spectral correlation is used to prediction in spectral direction for decorrelation of lossless compression of hyperspectral images. Experiments show the proposed algorithm is effective, and it's more important that it has much lower complexity than other algorithms.

Paper Details

Date Published: 11 January 2007
PDF: 7 pages
Proc. SPIE 6279, 27th International Congress on High-Speed Photography and Photonics, 627937 (11 January 2007); doi: 10.1117/12.725335
Show Author Affiliations
Liang Chen, Xi'an Research Institute of High Technology (China)
535 Hospital Huaihua (China)
Daizhi Liu, Xi'an Research Institute of High Technology (China)
Huazhong Univ. of Science and Technology (China)
Shiqi Huang, Xi'an Research Institute of High Technology (China)


Published in SPIE Proceedings Vol. 6279:
27th International Congress on High-Speed Photography and Photonics

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