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The linear prediction vector quantization for hyperspectral image compression

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Abstract

In this paper, a hyperspectral image compression method is proposed. It is based on spectral clustering, linear prediction and the vector quantization (VQ). Since the hyperspectral image has stronger spectral correlation than spatial correlation, the spectral clustering and model of linear prediction are introduced to reduce the spectral correlation. In the proposed method, spectral clustering algorithm of K-means is employed, and the centroids of clustered results are used as reference bands, then the reference bands are employed in the model of linear prediction to compute the prediction error, finally the prediction error is encoded by VQ. The experiments results using AVIRIS images are compared to IVQ and AR + SubPCA+JPEG2000 algorithm, the results show that our proposed algorithm outperforms other algorithms.

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Acknowledgments

This work is supported in part by the Open Research Fund of Key Laboratory of Spectral Imaging Technology, Chinese Academy of Sciences (Grant No. LSIT201606D) and the Open Project Program of the National Laboratory of Pattern Recognition (NLPR) (Grant No. 201800030).

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Xi’an Jiaotong University, No.28, Xianning West Road, Xi’an, 710049, People’s Republic of China

    Rui Li, Zhibin Pan & Yang Wang

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  1. Rui Li
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  2. Zhibin Pan
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  3. Yang Wang
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Correspondence to Zhibin Pan.

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Li, R., Pan, Z. & Wang, Y. The linear prediction vector quantization for hyperspectral image compression. Multimed Tools Appl 78, 11701–11718 (2019). https://doi.org/10.1007/s11042-018-6724-8

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  • DOI: https://doi.org/10.1007/s11042-018-6724-8

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