Inhalt

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   Zusammenfassung
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   Abstract
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   Contents
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  Introduction
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   Problem Statement
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   Contributions
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   Outline
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  1. Fundamentals
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   1.1 Terminology and Notation
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   1.2 Multispectral Image Processing – A General Overview
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   1.2.1 Acquisition
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   1.2.2 Data Analysis
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   1.2.3 Visualization
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  2. Design Goals – Requirements and Challenges
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   2.1 Application Scenarios
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   2.2 Requirements and Challenges
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   2.3 Prior Work
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   2.4 Discussion of Design Goals
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  3. Blur in Multispectral Image Data
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   3.1 Problem Discussion
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   3.1.1 Blur in Multispectral Imaging
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   3.1.2 Qualitative Evaluation of Consequences for Analysis
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  3.2 Conceptional Proposal of a Deblurring Approach
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   3.2.1 Related Work
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   3.2.2 Overview
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   3.2.3 Estimation of Point Spread Functions
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   3.2.4 Non-Blind Deblurring
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   3.2.5 A first Result for Multispectral Imaging
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   3.3 Summary
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   4. Multispectral Data Analysis
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  4.1 Similarity Measure in High-Dimensional Space
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   4.1.1 Background
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   4.1.2 Assessment of Attribute Variation
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   4.1.3 Generalized Partial Covariance
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   4.1.4 Application to Multispectral Image Data
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   4.1.5 Summary
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  4.2 Generic Determination of Constituent Spectra
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   4.2.1 Conceptional Overview
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   4.2.2 Semi-Automatic Endmember Extraction
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   4.2.3 Usage Examples
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   4.2.4 Summary
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  4.3 Outlier Masking for Endmember Extraction
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   4.3.1 Background
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   4.3.2 Outlier Masking
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   4.3.3 Usage Example
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   4.3.4 Summary
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  4.4 Unmixing Coefficients for Interactive Applications
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   4.4.1 Background
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   4.4.2 Progressive Unmixing Scheme
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   4.4.3 Prediction of New Coefficients for Refined Endmember Sets
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   4.4.4 OSP-Based Coefficient Estimation
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   4.4.5 Results
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   4.4.6 Summary
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   4.5 Summary
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   5. Multispectral Image Visualization
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  5.1 Complementary Visualization
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   5.1.1 Visualizations
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   5.1.2 Exploration Methods
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   5.1.3 Usage Examples
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   5.1.4 Summary
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  5.2 Expressive Spectral Error Visualization
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   5.2.1 Problem Statement
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   5.2.2 Conceptional Overview
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   5.2.3 Colored Distance Metrics
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   5.2.4 Spectral Error Classification
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   5.2.5 Interactive Exploration
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   5.2.6 Results
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   5.2.7 Summary
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  5.3 Radviz-based Multispectral Image Segmentation
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   5.3.1 Problem Statement
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   5.3.2 Conceptional Overview
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   5.3.3 Radviz-based Semi-Automatic Analysis
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   5.3.4 Cluster Evaluation and Refinement
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   5.3.5 Usage Examples
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   5.3.6 Summary
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   5.4 Summary
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  6. Application Example
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   6.1 Application Scenario
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   6.2 Summary
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   Summary and Conclusions
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   A Datasets
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  B Formulas
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   B.1 Distance Measures
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   B.2 Non-Negative Inverse Unmixing Operation
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   B.3 Color Matching Functions
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   B.4 Conversion from Radiance to Reflectance
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   Bibliography
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   List of Figures
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   List of Tables
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   Abbreviations
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   Symbols
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   Glossary