臺灣博碩士論文加值系統

It is difficult to obtain an accurate satellite imagery of the target landscapes. In recent years, a technique called Pansharpening method has demonstrated its potential to help obtain enhancing the quality of spatial and spectral satellite imagery. The SimiRegPS is one of the finest candidates to perform pansharpening method. Notice that the parameter selections, namely the regularizer parameter (λ) and penalty parameter (µ), are critical to acquire an ideal result for such a method, this thesis aims to determine the parameter setting for SimiRegPS method considering various target landscapes.
In developing the parameter setting, this thesis will use satellite image with four different characteristic landscapes (urban, mountain, crop and coastal land type), to find its specific parameter setting. The parameter setting is defined using trial and error methods, with a specific range of wider parameter range and ultimately discovering a narrower and defined range for each respective land types. The performance of the defined parameter setting is assessed using a quality index (visually and quantitatively) and compared with other pansharpening methods. The result shows that the defined parameter setting can lead to better results when compared to its default setting, and generally gives advantageous results against other pansharpening methods. It can be concluded that the parameter setting defined in this thesis can be applied to enhance the performance of the SimiRegPS method, and it is also revealed that for each land type, the ideal parameter range value would be different.

ABSTRACT I
ACKNOWLEDGEMENTS III
TABLE OF CONTENTS V
LIST OF TABLES VII
LIST OF FIGURES VIII
1. INTRODUCTION 1
1.1 Research Background. 1
2. LITERATURE REVIEW 4
2.1 Satellite imagery 4
2.2 Pansharpening method 6
2.2.1 Component Substitution 6
2.2.2 Multiresolution Analysis 8
2.3 Self-Similarity Regularization Pansharpening Method 11
2.4 Quality Index 14
2.4.1 Spectral Angle Mapper 15
2.4.2 Root Mean Square Error 16
2.4.3 Peak Signal to Noise Ratio 17
3. RESEARCH DESIGN AND METHODOLOGY 18
3.1 Satellite Imagery Dataset 18
3.2 SimiRegPS Parameter Selection 20
3.3 Parameter Setting Quality Index 22
4. RESEARCH RESULTS 25
4.1 Parameter Setting Selection 26
4.2 Quality Index 28
4.2.1 Urban land type 29
4.2.2 Mountain land type 31
4.2.3 Crop land type 33
4.2.4 Coastal land type 35
5. CONCLUSION 38
REFERENCES 40

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