Semantic Interpretation of Digital Aerial Images Utilizing ...

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10 Chapter 1. Introduction high-quality results from multiple observation for color, height and semantic interpretation. For each modality separately, optimization schemes are introduced for the fusion in an orthographic view. The evaluation section demonstrates the benefit of using the high redundancy provided by the aerial imagery. Chapter 5. In Chapter 5 we address the problem of semantic modeling by incorporating the holistic scene description obtained from Chapter 4. The main part of Chapter 5 covers the extraction, parametrization and modeling of rooftop landscapes from color information, derived building regions and surface models. A detailed evaluation shows the benefit of this interaction. Chapter 6. Finally, Chapter 6 concludes the thesis with a summary of the obtained outcomes and gives and ideas for future work, that could help to improve the suggested interpretation workflow.
Chapter 2 Digital Aerial Imagery This thesis aims for a largely automatic semantic interpretation of digitally mapped urban environments by utilizing massively redundant information of appearance and 3D scene information. While highly overlapping color images and corresponding geometric data are assumed to be given in advance, additional products are required to achieve the interpretation of aerial scenes. We thus outline involved modalities, ranging from redundant color and texture information, over models, representing the surface and the terrain, to estimated elevation measurements. Having the holistic collection of data applies to semantically describe entire virtual space, but also to automatically derive parameters individually specified for a certain task. In this thesis the proposed approaches largely use digital aerial images produced by the high-resolution camera Microsoft Ultra- CamD [Leberl et al., 2003]. The camera provides multi-view pixel observations, obtained by acquiring the aerial project with high overlaps, as well as multi-spectral information for mapped point, so that any applied algorithm can rely on highly redundant analysis. We first describe the digital aerial imagery and highlight the process of range image generation. This chapter further outlines the generation of additional input sources, such as the terrain model and the derivation of the elevation measurements, and discusses the orthographic image representation that is commonly used in aerial workflows. In addition, we introduce the aerial project Dallas, Graz and San Francisco. 2.1 Aerial Photography Digital aerial mapping has become feasible with the first large-format digital aerial cameras in the beginning of the 21st century. To compete with at that time traditionally used film images, these digital cameras had to offer an image width of at least of 11000 pixels and a color band including RGB and infrared channels [Leberl et al., 2003]. The first dig- 11
Page 1: PhD Thesis Semantic Interpretation
Page 5: Statutory Declaration I declare tha
Page 8 and 9: This thesis was created at the Inst
Page 11 and 12: Kurzfassung Eines der grundlegenden
Page 13 and 14: Contents 1 Introduction 1 1.1 Aeria
Page 15 and 16: CONTENTS iii 5.4.3 Prototype Refine
Page 17 and 18: Chapter 1 Introduction Three-dimens
Page 19 and 20: 1.2. Photo-realistic Modeling vs. V
Page 21 and 22: 1.3. Semantic Interpretation of Aer
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Page 25: 1.5. Outline 9 3. From Interpreted
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3.8. Experiments on Benchmark Datas
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3.9. Experiments on Aerial Images 6
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3.10. Discussion and Summary 73 Fig
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Chapter 4 From 3D to the Fusion of
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4.1. Introduction 81 Figure 4.1: Th
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4.2. Introducing a Common View 83 i
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4.3. Fusion of Redundant Intensity
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4.4. Fusion of Redundant Classifica
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4.4. Fusion of Redundant Classifica
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4.5. Experiments 95 Figure 4.6: A c
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4.5. Experiments 97 Figure 4.7: A s
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4.5. Experiments 99 Experiments on
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4.5. Experiments 101 Figure 4.10: A
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4.5. Experiments 105 As shown in Fi
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4.5. Experiments 109 Figure 4.18: S
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4.5. Experiments 111 Figure 4.20: C
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4.5. Experiments 113 Figure 4.22: O
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4.5. Experiments 115 the CRFs with
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4.5. Experiments 117 Figure 4.25: S
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4.5. Experiments 119 Dallas buildin
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Chapter 5 From Interpreted Regions
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5.1. Introduction 129 Figure 5.1: T
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5.3. Overview 131 cation techniques
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5.4. Building Modeling based on Sup
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5.5. Experiments 141 Dataset Image
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5.5. Experiments 143 Figure 5.9: 3D
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Chapter 6 Conclusion This thesis ha
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6.2. Outlook 151 can be successfull
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Appendix A Publications My work at
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155 (17) Leberl, F., Kluckner, S.,
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Appendix B Acronyms CRF Conditional
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List of Figures 1.1 A illustration
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LIST OF FIGURES 161 4.15 Obtained f
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List of Tables 2.1 The basic inform
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Bibliography [Agarwal et al., 2009]
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BIBLIOGRAPHY 167 [Champion and Bold
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BIBLIOGRAPHY 169 [Forsyth et al., 1
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BIBLIOGRAPHY 171 [Hoiem et al., 200
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BIBLIOGRAPHY 173 [Leibe et al., 200
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BIBLIOGRAPHY 175 [Ojala et al., 200
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BIBLIOGRAPHY 177 [Santner et al., 2
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BIBLIOGRAPHY 179 [Unnikrishnan et a
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