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Research of Remote Sensing Image Compression Technology Based on Compressed Sensing

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Advances in Image and Graphics Technologies (IGTA 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 525))

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Abstract

Compressed Sensing (CS) theory is a new method of signal acquisition and processing proposed in recent years. With small amount of sampling data recovering original data to precisely reconstruct sparse signal or compression signal, the theory breaks though the restriction of Nyquist sampling theorem. CS can avoid enormous sampling data waste but also reduce the complexity of image coding. This paper reviews the basic theory of CS and its three key points, including signal sparse representation, design of measurement matrix and reconstruction algorithms. Then, the application of CS in the field of remote sensing image compression technology is studied. Using MATLAB software, we do a series of CS emulation experiments compared with the traditional compression methods. The results show that the proposed method has a good performance on the remote sensing image compression.

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Author information

Authors and Affiliations

  1. Institute of Geospatial Information, Information Engineering University, Zhengzhou, 450000, China

    T Yu & Shujun Deng

Authors
  1. T Yu
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  2. Shujun Deng
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Corresponding author

Correspondence to T Yu .

Editor information

Editors and Affiliations

  1. Chinese Academy of Sciences Instute of Automation, Beijing, China

    Tieniu Tan

  2. Beijing Jiaotong University, Beijing, China

    Qiuqi Ruan

  3. Tsinghua University, Beijing, China

    Shengjin Wang

  4. Tsinghua University, Beijing, China

    Huimin Ma

  5. Chinese Academy of Sciences, Beijing, China

    Kaichang Di

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Yu, T., Deng, S. (2015). Research of Remote Sensing Image Compression Technology Based on Compressed Sensing. In: Tan, T., Ruan, Q., Wang, S., Ma, H., Di, K. (eds) Advances in Image and Graphics Technologies. IGTA 2015. Communications in Computer and Information Science, vol 525. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47791-5_25

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  • DOI: https://doi.org/10.1007/978-3-662-47791-5_25

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-47790-8

  • Online ISBN: 978-3-662-47791-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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