3D Data Compression Systems Based on Vector Quantization for Reducing the Data Rate of Hyperspectral Imagery

  • Shen-En Qian
  • Allan B. Hollinger
  • Dan Williams
  • Davinder Manak


The next generation of satellite-based remote sensing instruments will produce an unprecedented volume of data. Imaging spectrometers, also known as hyperspectral imagers, are prime examples. They collect image data in hundreds of spectral bands simultaneously from the near ultraviolet through the short wave infrared, and are capable of providing direct identification of surface materials. A schematic diagram illustrating the concept of an imaging spectrometer is given in Fig.11. The volume and complexity of data produced by these instruments offers a significant challenge to downlink transmission and traditional image analysis methods. Since they produce 3-dimensional (3D) data cubes in which two dimensions correspond to spatial position and the third to wavelength, raw data rates can easily exceed the available downlink capacity or on-board storage capacity. Often, therefore, a portion of the data collected on board is discarded before transmission. This data reduction process may involve: 1) reducing the duty cycle, 2) reducing the spatial or spectral resolution, and 3) reducing the spatial or spectral range. Obviously, in such cases large amounts of information are lost.


Root Mean Square Error Compression Ratio Data Compression Vector Quantization Data Cube 


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Copyright information

© Springer Science+Business Media New York 1997

Authors and Affiliations

  • Shen-En Qian
    • 1
    • 2
  • Allan B. Hollinger
    • 1
  • Dan Williams
    • 1
    • 3
  • Davinder Manak
    • 1
  1. 1.Canadian Space AgencySt-HubertCanada
  2. 2.State Key Laboratory of Applied Optics, Changchun Institute of Optics & Fine MechanicsChinese Academy of SciencesChangchunChina
  3. 3.MacDonald Dettwiler and AssociatesRichmondCanada

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