Decorrelate hyperspectral images using spectral correlation

Liang Chen; Daizhi Liu; Shiqi Huang

doi:10.1117/12.725335

11 January 2007 Decorrelate hyperspectral images using spectral correlation

Liang Chen, Daizhi Liu, Shiqi Huang

Proceedings Volume 6279, 27th International Congress on High-Speed Photography and Photonics; 627937 (2007) https://doi.org/10.1117/12.725335
Event: 27th International congress on High-Speed Photography and Photonics, 2006, Xi'an, China

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.

Citation Download Citation

Liang Chen, Daizhi Liu, and Shiqi Huang "Decorrelate hyperspectral images using spectral correlation", Proc. SPIE 6279, 27th International Congress on High-Speed Photography and Photonics, 627937 (11 January 2007); https://doi.org/10.1117/12.725335

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