Prior important band hyperspectral image compression

Feipeng Li; Haimai Shao; Guorui Ma; Qianqing Qin; Deren Li

doi:10.1117/12.538670

25 September 2003 Prior important band hyperspectral image compression

Feipeng Li, Haimai Shao, Guorui Ma, Qianqing Qin, Deren Li

Proceedings Volume 5286, Third International Symposium on Multispectral Image Processing and Pattern Recognition; (2003) https://doi.org/10.1117/12.538670
Event: Third International Symposium on Multispectral Image Processing and Pattern Recognition, 2003, Beijing, China

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.

Citation Download Citation

Feipeng Li, Haimai Shao, Guorui Ma, Qianqing Qin, and Deren Li "Prior important band hyperspectral image compression", Proc. SPIE 5286, Third International Symposium on Multispectral Image Processing and Pattern Recognition, (25 September 2003); https://doi.org/10.1117/12.538670

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