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Proceedings Paper

A new architecture for hyperspectral image compression based on wavelets transformation and fractal composition
Author(s): Xingtang Hu; Bing Zhang; Xia Zhang; Fangchao Hu; Zheng Wei
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Paper Abstract

A fractal-based image compression algorithm under wavelet transformation for hyper-spectral remote sensing image was introduced in this paper (also named AWFC algorithm). With the development of the hyperspectral remote sensing we have to obtain more and more spectral bands and how to store and transmit the huge data measured by TB bits level becomes a disaster to the limited electrical bandwidth. It is important to compress the huge hyperspectral image data acquired by hyperspectral sensor such as MODIS, PHI, OMIS etc. Otherwise, conventional lossless compression algorithm couldn't reach satisfied compression ratio while other loss compression methods could get results of high compression ratio but no good image fidelity especially to the hyperspectral image data. As the third generation image compression algorithm-fractal image compression is superior than traditional compression methods with high compression ratio, good image fidelity and less time complexity. In order to keep the spectral dimension invariability, we have compared the results of two compression algorithms based on the outside storage file structure of BSQ and BIP separately. The HV and Quad-tree partitioning and the domain-range matching algorithms have also been improved to accelerate the encode/decode efficiency. The proposed method has been realized and obtained perfect experimental results. At last, the possible modifications algorithm and the limitations of the method are also analyzed and discussed in this paper.

Paper Details

Date Published: 9 June 2006
PDF: 7 pages
Proc. SPIE 6200, Remote Sensing of the Environment: 15th National Symposium on Remote Sensing of China, 62000L (9 June 2006); doi: 10.1117/12.681725
Show Author Affiliations
Xingtang Hu, Institute of Remote Sensing Applications, CAS (China)
Bing Zhang, Institute of Remote Sensing Applications, CAS (China)
Xia Zhang, Institute of Remote Sensing Applications, CAS (China)
Fangchao Hu, Institute of Remote Sensing Applications, CAS (China)
Zheng Wei, Institute of Remote Sensing Applications, CAS (China)


Published in SPIE Proceedings Vol. 6200:
Remote Sensing of the Environment: 15th National Symposium on Remote Sensing of China
Qingxi Tong; Wei Gao; Huadong Guo, Editor(s)

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