Lossless Compression of Hyperspectral Images Using K-means Clustering and Conventional Recursive Least-squares Predictor

GAO Fang; SUN Changjian; SHAO Qinglong; GUO Shuxu

doi:10.11999/JEIT151439

Volume 38 Issue 11

Dec. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(11): 2709-2714

GAO Fang, SUN Changjian, SHAO Qinglong, GUO Shuxu. Lossless Compression of Hyperspectral Images Using K-means Clustering and Conventional Recursive Least-squares Predictor[J]. Journal of Electronics & Information Technology, 2016, 38(11): 2709-2714. doi: 10.11999/JEIT151439

Citation:

GAO Fang, SUN Changjian, SHAO Qinglong, GUO Shuxu. Lossless Compression of Hyperspectral Images Using K-means Clustering and Conventional Recursive Least-squares Predictor[J]. Journal of Electronics & Information Technology, 2016, 38(11): 2709-2714. doi: 10.11999/JEIT151439

Citation:

GAO Fang, SUN Changjian, SHAO Qinglong, GUO Shuxu. Lossless Compression of Hyperspectral Images Using K-means Clustering and Conventional Recursive Least-squares Predictor[J]. Journal of Electronics & Information Technology, 2016, 38(11): 2709-2714. doi: 10.11999/JEIT151439

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Lossless Compression of Hyperspectral Images Using K-means Clustering and Conventional Recursive Least-squares Predictor

doi: 10.11999/JEIT151439

Funds:

The National Natural Science Foundation of China (41101419)

Received Date: 2015-12-22
Rev Recd Date: 2016-04-08
Publish Date: 2016-11-19

Abstract

Abstract

To improve the compression ratio of lossless compression scheme based on prediction, a lossless compression scheme for hyperspectral images using K-means Clustering method and Conventional Recursive Least-Squares (C-CRLS) predictor is presented in this paper. The proposed scheme first clusters the spectral data into clusters according to their spectra using the famous K-means clustering method. Then, the proposed scheme calculates the preliminary estimates to form the input vector of the conventional recursive least-squares predictor. Finally, after prediction, the prediction residuals are sent to the arithmetic coder. Experiments on the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) 2006 hyperspectral images show that the proposed scheme yields an average compression ratio of 4.63, 2.82, and 4.77 on the 16-bit calibrated images, the 16-bit uncalibrated images, and the 12-bit uncalibrated images, respectively. Experimental results demonstrate that the proposed scheme outperforms other current state-of-the-art schemes for hyperspectral images that have been previously reported.
- Hyperspectral images,
- Image compression,
- Recursive least-squares,
- Clustering

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References(23)

References

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