Image Pan-sharpening with Markov Random Field and Simulated Annealing

106 Pages·2007·5.13 MB·English

Image Pan-sharpening with
Markov Random Field and
Simulated Annealing
Hailemariam Gedlu Kitaw
March, 2007

Image Pan-sharpening with Markov Random
Field and
Simulated Annealing
by
Hailemariam Gedlu Kitaw
ThesissubmittedtotheInternationalInstituteforGeo-informationScienceand
Earth Observation in partial fulfilment of the requirements for the degree of
Master of Science in Geo-information Science and Earth Observation, Speciali-
sation: Geoinformatics.
Thesis Assessment Board
Thesis advisors Dr. V. A. Tolpekin
Prof. Dr. Ir. A. Stein
Assessment Board Chair Prof. Dr. J.L. van Genderen
External examiner Dr. Ir. L.J. Spreeuwers
INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION
ENSCHEDE, THE NETHERLANDS
Disclaimer
This document describes work undertaken as part of a programme of study at
theInternationalInstituteforGeo-informationScienceandEarthObservation
(ITC). All views and opinions expressed therein remain the sole responsibility
of the author, and do not necessarily represent those of the institute.
Abstract
Asmanyearthobservationsatellitesprovidebothhighresolutionpanchro-
matic and low resolution multispectral images, pan-sharpening is an im-
portant technique in the field of remote sensing to produce a high reso-
lution multispectral image for a given low resolution multispectral image
and a higher resolution panchromatic image of the same area. To date,
several image pan-sharpening techniques like the IHS method, the PCA
method, the wavelet method, etc have been developed. However for the
recent high and very high resolution satellite images the developed algo-
rithms can hardly produce satisfactory results [44, 45]. There are signifi-
cantcolordistortionsinthepan-sharpenedimageandthequalityofthese
pan-sharpenedimagesisalsoseldomquestioned.
This research concentrates on developing a new method for image pan-
sharpening in Bayesian image restoration framework using Markov Ran-
dom Field (MRF) and simulated annealing that provide maximum a pos-
teriori (MAP) estimate of the pan-sharpened image. The study models the
(unknown)pan-sharpenedimagewithanMRF(priorprobability)andmod-
elsobservationprocessforthepanchromaticandthecoarseresolutionmul-
tispectral images (conditional probability). These probabilities are com-
binedtoderiveposteriorprobabilityforthepan-sharpenedimage. Optimal
solution is sought as the image that maximizes the posterior probability.
Thesimulatedannealingalgorithmisusedtoobtaintheoptimalsolution.
In order to test the performance of the method, a reference image is cho-
sen and is assumed as the unknown pan-sharpened image. The reference
imageisdegradedtoproducepanchromaticandmultispectralimages. The
methodisthenappliedtorestorethepan-sharpenedimagefromthesede-
graded images and the accuracy assessment is performed by comparing
itwiththereferenceimage. Themethodshowsaveryhighcorrelationbe-
tweentherestoredpan-sharpenedimageandthereferenceimage. Theper-
formance of the proposed method is compared with other pan-sharpening
methodsandisprovedthatitperformsbetterthanexistingmethods.
Keywords
super resolution, QuickBird, Bayesian image restoration, IKONOS, high
resolution, image fusion, Pan-sharpening, Simulated Annealing, Markov
RandomField(MRF)
i
Abstract
ii
Contents
Abstract i
List of Tables vii
List of Figures ix
1 Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.1 Image resolution . . . . . . . . . . . . . . . . . . . . . . . 4
1.2 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 Research objectives . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.4 Research questions . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.5 Research setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.5.1 Phase 1 - Pre-processing . . . . . . . . . . . . . . . . . . . 7
1.5.2 Phase 2 - Modeling the prior information . . . . . . . . . 8
1.5.3 Phase 3 - Modeling the conditional information . . . . . 8
1.5.4 Phase 4 - Estimation of the MAP . . . . . . . . . . . . . . 9
1.5.5 Phase5-QualityAssessmentandperformancecomparison 9
1.6 Structure of the thesis . . . . . . . . . . . . . . . . . . . . . . . . 9
2 Literature review 11
2.1 Previous works of MRF in remote sensing . . . . . . . . . . . . . 11
2.2 Image pan-sharpening techniques . . . . . . . . . . . . . . . . . 12
2.2.1 The Intensity-Hue-Saturation (IHS) method . . . . . . . 12
2.2.2 The principal component analysis (PCA) method . . . . . 13
2.2.3 The Brovey transform method . . . . . . . . . . . . . . . . 14
2.2.4 The wavelet methods . . . . . . . . . . . . . . . . . . . . . 14
2.2.5 Method of Price and Method of Park & Kang . . . . . . . 15
2.2.6 The Bayesian methods . . . . . . . . . . . . . . . . . . . . 16
2.2.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3 Data and pre-processing 21
3.1 Synthetic data sets . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.1.1 Synthetic data I . . . . . . . . . . . . . . . . . . . . . . . . 21
3.1.2 Synthetic data II (from IKONOS image) . . . . . . . . . . 25
3.2 Remote sensing data sets . . . . . . . . . . . . . . . . . . . . . . . 28
3.2.1 QuickBird image . . . . . . . . . . . . . . . . . . . . . . . 28
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Contents
3.2.2 Aerial Photograph . . . . . . . . . . . . . . . . . . . . . . . 30
4 MRF & simulated annealing-based pan-sharpening method 33
4.1 Neighborhood system and MRF & Gibbs random fields . . . . . 33
4.1.1 Neighborhood system . . . . . . . . . . . . . . . . . . . . . 33
4.1.2 MRF and Gibbs random fields . . . . . . . . . . . . . . . . 34
4.2 Modeling prior energy . . . . . . . . . . . . . . . . . . . . . . . . 36
4.3 Modeling conditional energies . . . . . . . . . . . . . . . . . . . . 36
4.3.1 Conditional energy from the panchromatic image . . . . 36
4.3.2 Conditional energy from the multispectral image . . . . 37
4.4 Global energy Construction and optimization . . . . . . . . . . . 38
4.4.1 Initial pan-sharpened image generation . . . . . . . . . . 39
4.4.2 Simulated annealing algorithm . . . . . . . . . . . . . . . 40
4.4.3 Gibbs sampler . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.4.4 Optimizationofthepan-sharpenedimagewithSA&Gibbs
sampler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.5 Accuracy assessment . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.5.1 Correlation coefficient . . . . . . . . . . . . . . . . . . . . 45
4.5.2 Root mean square error . . . . . . . . . . . . . . . . . . . 46
5 Results and Discussions 47
5.1 Experimental results from the synthetic data I . . . . . . . . . . 47
5.2 Experimental results from synthetic data II . . . . . . . . . . . . 49
5.2.1 Effect of initial temperature T . . . . . . . . . . . . . . . 49
0
5.2.2 Effect of temperature updating factor σ . . . . . . . . . . 51
5.2.3 Effect of smoothness parameter λ . . . . . . . . . . . . . . 52
5.2.4 Effect of neighborhood size W . . . . . . . . . . . . . . 53
size
5.2.5 Effect of parameters β & γ . . . . . . . . . . . . . . . . . . 54
5.2.6 Effect of parameter ρ . . . . . . . . . . . . . . . . . . . . . 56
5.2.7 Control experiment for the initial temperature T . . . . 57
0
5.3 Pan-sharpening from the QuickBird image . . . . . . . . . . . . 59
5.4 Accuracyassessmentandperformancecomparisonwithotherpan-
sharpening methods . . . . . . . . . . . . . . . . . . . . . . . . . 62
5.5 Summary of findings from results . . . . . . . . . . . . . . . . . . 64
6 Conclusion and recommendations 67
6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
6.1.1 Model formulation, optimal parameter determination and
optimization . . . . . . . . . . . . . . . . . . . . . . . . . . 67
6.1.2 Theperformanceofthemethodandcomparisonwithother
methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
6.2 Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
Bibliography 71
Appendices 74
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Contents
A Summaryoftheresultinsearchingoptimalinitialtemperature
(T ) value 75
0
B Summaryoftheresultinsearchingoptimaltemperatureupdat-
ing factor (σ) value 79
C Summaryoftheresultinsearchingoptimalsmoothnessparam-
eter (λ) value 83
D Summary of the result in searching optimal window size value 87
E Summaryoftheresultinsearchingoptimalinitialtemperature
(T ) value using optimal values from synthetic data II 89
0
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Contents
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