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Labitzke, Björn: Visualization and analysis of multispectral image dataVisualisierung und Analyse multispektraler Bilddaten. 2013
Inhalt
Zusammenfassung
Abstract
Contents
Introduction
Problem Statement
Contributions
Outline
1. Fundamentals
1.1 Terminology and Notation
1.2 Multispectral Image Processing – A General Overview
1.2.1 Acquisition
1.2.2 Data Analysis
1.2.3 Visualization
2. Design Goals – Requirements and Challenges
2.1 Application Scenarios
2.2 Requirements and Challenges
2.3 Prior Work
2.4 Discussion of Design Goals
3. Blur in Multispectral Image Data
3.1 Problem Discussion
3.1.1 Blur in Multispectral Imaging
3.1.2 Qualitative Evaluation of Consequences for Analysis
3.2 Conceptional Proposal of a Deblurring Approach
3.2.1 Related Work
3.2.2 Overview
3.2.3 Estimation of Point Spread Functions
3.2.4 Non-Blind Deblurring
3.2.5 A first Result for Multispectral Imaging
3.3 Summary
4. Multispectral Data Analysis
4.1 Similarity Measure in High-Dimensional Space
4.1.1 Background
4.1.2 Assessment of Attribute Variation
4.1.3 Generalized Partial Covariance
4.1.4 Application to Multispectral Image Data
4.1.5 Summary
4.2 Generic Determination of Constituent Spectra
4.2.1 Conceptional Overview
4.2.2 Semi-Automatic Endmember Extraction
4.2.3 Usage Examples
4.2.4 Summary
4.3 Outlier Masking for Endmember Extraction
4.3.1 Background
4.3.2 Outlier Masking
4.3.3 Usage Example
4.3.4 Summary
4.4 Unmixing Coefficients for Interactive Applications
4.4.1 Background
4.4.2 Progressive Unmixing Scheme
4.4.3 Prediction of New Coefficients for Refined Endmember Sets
4.4.4 OSP-Based Coefficient Estimation
4.4.5 Results
4.4.6 Summary
4.5 Summary
5. Multispectral Image Visualization
5.1 Complementary Visualization
5.1.1 Visualizations
5.1.2 Exploration Methods
5.1.3 Usage Examples
5.1.4 Summary
5.2 Expressive Spectral Error Visualization
5.2.1 Problem Statement
5.2.2 Conceptional Overview
5.2.3 Colored Distance Metrics
5.2.4 Spectral Error Classification
5.2.5 Interactive Exploration
5.2.6 Results
5.2.7 Summary
5.3 Radviz-based Multispectral Image Segmentation
5.3.1 Problem Statement
5.3.2 Conceptional Overview
5.3.3 Radviz-based Semi-Automatic Analysis
5.3.4 Cluster Evaluation and Refinement
5.3.5 Usage Examples
5.3.6 Summary
5.4 Summary
6. Application Example
6.1 Application Scenario
6.2 Summary
Summary and Conclusions
A Datasets
B Formulas
B.1 Distance Measures
B.2 Non-Negative Inverse Unmixing Operation
B.3 Color Matching Functions
B.4 Conversion from Radiance to Reflectance
Bibliography
List of Figures
List of Tables
Abbreviations
Symbols
Glossary