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Lossless Compression of Hyperspectral Images Using K-means Clustering and Conventional Recursive Least-squares Predictor |
GAO Fang SUN Changjian SHAO Qinglong GUO Shuxu |
(College of Electronic Science and Engineering, Jilin University, Changchun 130012, China) |
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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.
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Received: 22 December 2015
Published: 25 May 2016
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Fund: The National Natural Science Foundation of China (41101419) |
Corresponding Authors:
GUO Shuxu
E-mail: guosx@jlu.edu.cn
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