LowComplexity Lossless Compression of Hyperspectral Imagery via Linear Prediction

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Low-Complexity Lossless Compression of
Hyperspectral Imagery via Linear Prediction

• By Fei Nan Hani Saad
• Presented to Dr. Donald Adjeroh

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Index

• Hyperspectral Images, what are they?
• Remote Sensors and Low-complexity Image
  Compression
• Linear Prediction (LP)
• Spectral Oriented Least Squares (SLSQ)
• LP Implementation
• SLSQ Implementation
• Experimental Results
• Improvements
• References

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Hyperspectral Images

• High-definition electro-optic images
• Used in surveillance, geology, environmental
  monitoring, and meteorology
• 224 contiguous bands
• 3 or more consecutive
• scenes

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Remote Sensors Low-complexity Image Compression

• Hyperspectral sensors measure hundreds of
  wavelengths
• Airborne vs. Satellite Sensors
• Why low-complexity compression?

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Linear Prediction (LP)

• Spatial correlation
• Spectral correlation
• LP
• Interband linear prediction for interband coding
• Standard median predicton for intraband coding

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Linear Prediction contd

• Standard median predicton
• Used for intraband coding

Xi,j-1,k
Xi-1,j-1,k
Xi,j,k
Xi-1,j,k
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Linear Prediction contd

• Interband linear prediction
• Used for interband coding

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Spectral Oriented Least Squares (SLSQ)

• Prediction defined in two different enumerations
  for pixel
• Intraband enumeration
• Interband enumeration

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LP Implementation

• The first 2 conds apply to Interband. 2nd cond
  can be skip when T, given T gives best
  performance.
• The 3rd cond applies to Intraband(IB).

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SLSQ Implementation
The distance of Interband and intraband are
defined. The Predictor Error Matrix C and Matrix
X The simplified form when we assigned M4 and
N1.
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Experimental Results
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Experimental Results contd

• 128x128x224

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Improvements

• Using M5 vs. M4
• Keeping N1
• Future improvements can include look-ahead
  prediction

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References

• Randall B. Smith, Ph.D., 17 September 2001.
  MicroImages, Inc. Introduction to Hyperspectral
  Imaging with TNTmips. www.microimages.com
• Peg Shippert, Ph.D., Earth Science Applications
  Specialist Research Systems, Inc. Introduction to
  Hyperspectral Image Analysis.
• Suresh Subramanian,, Nahum Gat, Alan Ratcliff ,
  Michael Eismann. Real-time Hyperspectral Data
  Compression Using Principal Components
  Transformation