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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) - PowerPoint PPT Presentation
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Low-Complexity Lossless Low-Complexity Lossless Compression of Hyperspectral Compression of Hyperspectral Imagery via Linear PredictionImagery via Linear Prediction
By:By: Fei Nan Fei Nan& Hani Saad& Hani Saad
Presented to:Presented to: Dr. Donald Adjeroh Dr. Donald Adjeroh
Hyperspectral Image CompressionHyperspectral Image Compression 22
IndexIndex Hyperspectral Images, what are they?Hyperspectral Images, what are they? Remote Sensors and Low-complexity Remote Sensors and Low-complexity
Image CompressionImage Compression Linear Prediction (LP)Linear Prediction (LP) Spectral Oriented Least Squares (SLSQ)Spectral Oriented Least Squares (SLSQ) LP ImplementationLP Implementation SLSQ ImplementationSLSQ Implementation Experimental ResultsExperimental Results ImprovementsImprovements ReferencesReferences
Hyperspectral Image CompressionHyperspectral Image Compression 33
Hyperspectral ImagesHyperspectral Images
High-definition electro-optic High-definition electro-optic imagesimages
Used in surveillance, geology, Used in surveillance, geology, environmental monitoring, and environmental monitoring, and meteorologymeteorology
224 contiguous bands224 contiguous bands 3 or more consecutive 3 or more consecutive
scenesscenes
Hyperspectral Image CompressionHyperspectral Image Compression 44
Remote Sensors & Low-complexity Remote Sensors & Low-complexity Image CompressionImage Compression
Hyperspectral sensors measure hundreds of wavelengths
Airborne vs. Satellite Sensors Why low-complexity compression?
Hyperspectral Image CompressionHyperspectral Image Compression 55
Linear Prediction (LP)Linear Prediction (LP)
Spatial correlationSpatial correlation Spectral correlationSpectral correlation LPLP
• Interband linear prediction for interband Interband linear prediction for interband codingcoding
• Standard median predicton for Standard median predicton for intraband codingintraband coding
Hyperspectral Image CompressionHyperspectral Image Compression 66
Linear Prediction cont’dLinear Prediction cont’d
Standard median predictonStandard median predicton• Used for intraband codingUsed for intraband coding
Xi,j,kXi-1,j,k
Xi,j-1,kXi-1,j-1,k
Hyperspectral Image CompressionHyperspectral Image Compression 77
Linear Prediction cont’dLinear Prediction cont’d
Interband linear predictionInterband linear prediction• Used for interband codingUsed for interband coding
Hyperspectral Image CompressionHyperspectral Image Compression 88
Spectral Oriented Least Squares Spectral Oriented Least Squares (SLSQ)(SLSQ)
Prediction defined in two different Prediction defined in two different enumerations for pixel:enumerations for pixel:
1.1. Intraband enumerationIntraband enumeration
2.2. Interband enumerationInterband enumeration
Hyperspectral Image CompressionHyperspectral Image Compression 99
LP ImplementationLP Implementation
The first 2 conds apply to The first 2 conds apply to Interband.Interband. 2 2ndnd cond can be skip when T=cond can be skip when T=œœ, given T gives , given T gives best performance.best performance.
The 3The 3rdrd cond applies to cond applies to Intraband(IB). Intraband(IB).
Hyperspectral Image CompressionHyperspectral Image Compression 1010
SLSQ ImplementationSLSQ 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 CompressionHyperspectral Image Compression 1111
Experimental ResultsExperimental Results
Hyperspectral Image CompressionHyperspectral Image Compression 1212
Experimental Results cont’dExperimental Results cont’d
128x128x224128x128x224LPLP SLSQSLSQ
CupriteCuprite 1.9181.918 2.4252.425
JasperJasper 1.8501.850 2.3642.364
Low Low AltitudeAltitude
1.7081.708 2.1312.131
Lunar Lunar LakeLake
2.0652.065 2.3902.390
Hyperspectral Image CompressionHyperspectral Image Compression 1313
ImprovementsImprovements
Using M=5 vs. M=4Using M=5 vs. M=4 Keeping N=1Keeping N=1
Future Future improvements can improvements can include look-ahead include look-ahead predictionprediction
SLSQ2SLSQ2 SLSQ1SLSQ1CupriteCuprite 2.4432.443 2.4252.425
JasperJasper 2.3582.358 2.3642.364
Low Low AltitudeAltitude
2.1292.129 2.1312.131
Lunar Lunar LakeLake
2.4132.413 2.3902.390
AverageAverage 2.332.33 2.322.32
Hyperspectral Image CompressionHyperspectral Image Compression 1414
ReferencesReferences Randall B. Smith, Ph.D., 17 September 2001. Randall B. Smith, Ph.D., 17 September 2001.
MicroImages, Inc. Introduction to Hyperspectral MicroImages, Inc. Introduction to Hyperspectral Imaging with TNTmips. Imaging with TNTmips. www.microimages.comwww.microimages.com
Peg Shippert, Ph.D., Earth Science Applications Peg Shippert, Ph.D., Earth Science Applications Specialist Research Systems, Inc. Introduction Specialist Research Systems, Inc. Introduction to Hyperspectral Image Analysis.to Hyperspectral Image Analysis.
Suresh Subramanian,, Nahum Gat, Alan Suresh Subramanian,, Nahum Gat, Alan Ratcliff , Michael Eismann. Real-time Ratcliff , Michael Eismann. Real-time Hyperspectral Data Compression Using Principal Hyperspectral Data Compression Using Principal Components TransformationComponents Transformation
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