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Hyperspectral image compression: adapting SPIHT and EZW to anisotropic 3-D wavelet coding.

Emmanuel Christophe1, Corinne Mailhes, Pierre Duhamel

  • 1CNES (French Space Agency), Toulouse, France. e.christophe@ieee.org

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|November 14, 2008
PubMed
Summary

This study optimizes wavelet compression for hyperspectral images, developing a near-optimal decomposition that enhances efficiency. This method improves upon existing techniques, enabling better data compression for space missions.

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Area of Science:

  • Image processing
  • Data compression
  • Remote sensing

Background:

  • Hyperspectral images require efficient compression due to large data volumes.
  • Wavelet-based compression offers adaptability and reasonable complexity for such data.
  • Existing wavelet methods have been applied to hyperspectral space missions.

Purpose of the Study:

  • To optimize a full wavelet compression system specifically for hyperspectral images.
  • To define an optimal 3-D wavelet decomposition in a rate-distortion sense.
  • To adapt and compare zerotree coding methods (EZW, SPIHT) and JPEG 2000.

Main Methods:

  • Developed an algorithm for optimal 3-D wavelet decomposition.
  • Identified a fixed decomposition with comparable performance and lower complexity.

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  • Adapted Extended Zerotree Wavelet (EZW) and Set Partitioning In Hierarchical Trees (SPIHT) algorithms.
  • Compared performance against JPEG 2000 on diverse hyperspectral datasets.

    • Main Results:

    • A specific fixed wavelet decomposition was found to be near-optimal and significantly better than isotropic decomposition.
    • This decomposition enables efficient zerotree coding algorithms.
    • Adapted EZW and SPIHT methods showed competitive performance compared to JPEG 2000.

    Conclusions:

    • The optimized wavelet compression system offers improved efficiency for hyperspectral images.
    • The proposed fixed decomposition and zerotree coding provide a practical and effective compression solution.
    • This work contributes to better data management for hyperspectral remote sensing applications.