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Proceedings Paper

Prior important band hyperspectral image compression
Author(s): Feipeng Li; Haimai Shao; Guorui Ma; Qianqing Qin; Deren Li
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Paper Abstract

This paper presents a Prior Important Band (PIB) algorithm for the compression of hyper-spectral images. The PIB method endows some of the bands with high priority so that the quality of these bands after compression is better than other bands. The rationale behind this approach is that, the bands of a data cube have different amount of information. Some bands contain much more information and features than other bands. In the PIB algorithm, all bands are classified into four categories according to their importance and easiness for compression. For the simplicity of the compression algorithm, we choose spectral correlation and information amount as the main index. Bands of low spectral correlation and high information are selected as Important Bands. The benefit of this algorithm lies in that it treats the important bands with higher quality quantization, and other bands with comparatively low quality quantization, so that the information can be better preserved after compression. Experimental results illustrate that PIB hyper-spectral image compression algorithm would be suitable for most applications.

Paper Details

Date Published: 25 September 2003
PDF: 4 pages
Proc. SPIE 5286, Third International Symposium on Multispectral Image Processing and Pattern Recognition, (25 September 2003); doi: 10.1117/12.538670
Show Author Affiliations
Feipeng Li, Wuhan Univ. (China)
Haimai Shao, Wuhan Univ. (China)
Guorui Ma, Wuhan Univ. (China)
Qianqing Qin, Wuhan Univ. (China)
Deren Li, Wuhan Univ. (China)

Published in SPIE Proceedings Vol. 5286:
Third International Symposium on Multispectral Image Processing and Pattern Recognition
Hanqing Lu; Tianxu Zhang, Editor(s)

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