Semantic Interpretation of Digital Aerial Images Utilizing ...

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kann. Mittels synthetisch erzeugter Modelle untersuchen wir in Experimenten die erzielten Ergebnisse im Hinblick auf vorliegender Redundanz, korrekt zugeordneter Objektklassen, sowie erzielter Rauschunterdrückung. Für die Auswertung nutzen wir neben Vergleichsdatensätzen, reale Luftbildaufnahmen von Dallas, Graz und San Francisco.
Contents 1 Introduction 1 1.1 Aerial Photogrammetry . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Photo-realistic Modeling vs. Virtual Cities . . . . . . . . . . . . . . . . . 3 1.3 Semantic Interpretation of Aerial Images . . . . . . . . . . . . . . . . . . 4 1.4 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.5 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2 Digital Aerial Imagery 11 2.1 Aerial Photography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2 Redundancy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.3 Digital Surface Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.4 Digital Terrain Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.5 Orthographic Image Representation . . . . . . . . . . . . . . . . . . . . 20 2.6 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3 From Appearance and 3D to Interpreted Image Pixels 25 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.2 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.3 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.4 Feature Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.4.1 Mean and Covariance Descriptors . . . . . . . . . . . . . . . . . 32 3.4.2 Working with Symmetric Positive-Definite Matrices . . . . . . . 33 3.4.3 Generating Correlated Samples . . . . . . . . . . . . . . . . . . 38 3.4.4 Sigma Points Representation . . . . . . . . . . . . . . . . . . . . 40 3.4.5 Feature Representation for the Semantic Interpretation . . . . . . 41 3.4.6 Integral Image Structures . . . . . . . . . . . . . . . . . . . . . . 42 i
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: Kurzfassung Eines der grundlegenden
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
Page 23 and 24: 1.4. Contributions 7 Figure 1.4: Ov
Page 25 and 26: 1.5. Outline 9 3. From Interpreted
Page 27 and 28: Chapter 2 Digital Aerial Imagery Th
Page 29 and 30: 2.2. Redundancy 13 Figure 2.1: Geom
Page 31 and 32: 2.3. Digital Surface Model 15 Figur
Page 33 and 34: 2.3. Digital Surface Model 17 Since
Page 35 and 36: 2.3. Digital Surface Model 19 Figur
Page 37 and 38: 2.5. Orthographic Image Representat
Page 39 and 40: 2.7. Summary 23 Dataset Nb of Image
Page 41 and 42: Chapter 3 From Appearance and 3D to
Page 43 and 44: 3.2. Overview 27 Figure 3.2: Corres
Page 45 and 46: 3.3. Related Work 29 tation workflo
Page 47 and 48: 3.4. Feature Representation 31 a co
Page 49 and 50: 3.4. Feature Representation 33 cons
Page 51 and 52: 3.4. Feature Representation 35 d LE
Page 53 and 54: 3.4. Feature Representation 37 wher
Page 55 and 56: 3.4. Feature Representation 39 Figu
Page 57 and 58: 3.4. Feature Representation 41 Figu
Page 59 and 60: 3.4. Feature Representation 43 for
Page 61 and 62: 3.5. Randomized Forest Classifier 4
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3.6. Introducing Segmentation for O
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3.7. Refined Labeling 49 high compu
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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 103 Figure 4.12: I
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4.5. Experiments 105 As shown in Fi
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4.5. Experiments 107 Figure 4.16: A
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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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