Create Presentation
Download Presentation

Low-Complexity Lossless Compression of Hyperspectral Imagery via Linear Prediction

Low-Complexity Lossless Compression of Hyperspectral Imagery via Linear Prediction

166 Views

Download Presentation
Download Presentation
## Low-Complexity Lossless Compression of Hyperspectral Imagery via Linear Prediction

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -

**Low-Complexity Lossless Compression of Hyperspectral Imagery**via Linear Prediction By: Fei Nan& Hani Saad Presented to: Dr. Donald Adjeroh**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**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**Remote Sensors & Low-complexity Image Compression**• Hyperspectral sensors measure hundreds of wavelengths • Airborne vs. Satellite Sensors • Why low-complexity compression? Hyperspectral Image Compression**Linear Prediction (LP)**• Spatial correlation • Spectral correlation • LP • Interband linear prediction for interband coding • Standard median predicton for intraband coding Hyperspectral Image Compression**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**Linear Prediction cont’d**• Interband linear prediction • Used for interband coding Hyperspectral Image Compression**Spectral Oriented Least Squares (SLSQ)**Prediction defined in two different enumerations for pixel: • Intraband enumeration • Interband enumeration Hyperspectral Image Compression**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**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**Experimental Results**Hyperspectral Image Compression**Experimental Results cont’d**128x128x224 Hyperspectral Image Compression**Improvements**• Using M=5 vs. M=4 • Keeping N=1 • Future improvements can include look-ahead prediction Hyperspectral Image Compression**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