Low-Complexity Lossless Compression of Hyperspectral Imagery via Linear Prediction - PowerPoint PPT Presentation

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Low-Complexity Lossless Compression of Hyperspectral Imagery via Linear Prediction

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  1. Low-Complexity Lossless Compression of Hyperspectral Imagery via Linear Prediction By: Fei Nan& Hani Saad Presented to: Dr. Donald Adjeroh

  2. 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 Hyperspectral Image Compression

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

  4. Remote Sensors & Low-complexity Image Compression • Hyperspectral sensors measure hundreds of wavelengths • Airborne vs. Satellite Sensors • Why low-complexity compression? Hyperspectral Image Compression

  5. Linear Prediction (LP) • Spatial correlation • Spectral correlation • LP • Interband linear prediction for interband coding • Standard median predicton for intraband coding Hyperspectral Image Compression

  6. Linear Prediction cont’d • Standard median predicton • Used for intraband coding Xi-1,j-1,k Xi,j-1,k Xi,j,k Xi-1,j,k Hyperspectral Image Compression

  7. Linear Prediction cont’d • Interband linear prediction • Used for interband coding Hyperspectral Image Compression

  8. Spectral Oriented Least Squares (SLSQ) Prediction defined in two different enumerations for pixel: • Intraband enumeration • Interband enumeration Hyperspectral Image Compression

  9. LP Implementation • The first 2 conds apply to Interband. 2nd cond can be skip when T=œ, given T gives best performance. • The 3rd cond applies to Intraband(IB). Hyperspectral Image Compression

  10. SLSQ Implementation The distance of Interband and intraband are defined. The Predictor Error Matrix C and Matrix X The simplified form when we assigned M=4 and N=1. Hyperspectral Image Compression

  11. Experimental Results Hyperspectral Image Compression

  12. Experimental Results cont’d 128x128x224 Hyperspectral Image Compression

  13. Improvements • Using M=5 vs. M=4 • Keeping N=1 • Future improvements can include look-ahead prediction Hyperspectral Image Compression

  14. 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 Hyperspectral Image Compression