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PolSAR image compression based on online sparse K-SVD dictionary learning

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Abstract

we present a novel polarimetric synthetic aperture radar (PolSAR) image compression scheme. PolSAR data contains lots of similar redundancies in single-channel and massively correlation between polarimetric channels. So these features make it difficult to represent PolSAR data efficiently. In this paper, discrete cosine transform (DCT) is adopted to remove redundancies between polarimetric channels, simple but quite efficient in improving compressibility. Sparse K-singular value decomposition (K-SVD) dictionary learning algorithm is utilized to remove redundancies within each channel image. Double sparsity scheme will be able to achieve fast convergence and low representation error by using a small number of sparsity dictionary elements, which is beneficial for the task of PolSAR image compression. Experimental results demonstrate that both numerical evaluation indicators and visual effect of reconstructed images outperform other methods, such as SPIHT, JPEG2000, and offline method.

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Acknowledgements

This work is supported by the China Postdoctoral Science Foundation Special funded project (No.2012 T50799), the International Postdoctoral Exchange Fellowship Program 2013 by the Office of China Postdoctoral Council (No. 20130026) and the Open Research Fund of Key Laboratory of Spectral Imaging Technology by Chinese Academy of Sciences.

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Authors and Affiliations

  1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, Shaanxi Province, 710071, China

    Jing Bai, Bin Liu & Licheng Jiao

  2. Shandong Provincial Key Laboratory of Network-Based Intelligent Computing, University of Jinan, ChinaShandong Province, Jinan, 250022, China

    Lei Wang

Authors
  1. Jing Bai
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  2. Bin Liu
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  3. Lei Wang
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  4. Licheng Jiao
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Correspondence to Jing Bai.

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Bai, J., Liu, B., Wang, L. et al. PolSAR image compression based on online sparse K-SVD dictionary learning. Multimed Tools Appl 76, 24859–24870 (2017). https://doi.org/10.1007/s11042-017-4640-y

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  • DOI: https://doi.org/10.1007/s11042-017-4640-y

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