Semantic Interpretation of Digital Aerial Images Utilizing ...

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14 Chapter 2. Digital Aerial Imagery Figure 2.2: Sixty images taken from the dataset Dallas. The scene is taken from highly overlapping viewpoints. In this work, we utilize the geometric redundancy to produced a holistic description of urban scenes. images (16 bits color) with 80%/60% overlaps and a pixel size of 12.5 cm, resulting a data volume of approximately 1.5 GBytes. This is an increase of the data quantity by two orders of magnitude and is at the core of achieving full automation of aerial mapping. Figure 2.2 depicts sixty highly overlapping images taken from Dallas. The high geometric redundancy is obtained by using forward and side overlaps, so that every point in the terrain is imaged at least 10 times, and any algorithm can rely on multiple analysis results that then can either reinforce or cancel one another. Figure 2.3 shows a set of redundant scene observations taken from Graz. Every point on ground is mapped multiple times from different views. The proposed approaches make use of the high redundancy. The geometric redundancy additionally gets augmented by a radiometric redundancy
2.3. Digital Surface Model 15 Figure 2.3: Some redundant observations of a scene located in Graz. The mapped area is shown from different camera viewpoints, which are defined by the flight direction. In our approach we integrate overlapping information within a common orthographic view. using four spectral bands, adding an infrared band to the classical red, green and blue color channels. Moreover, the high-resolution color images offer sophisticated processing steps that synthetically increase the redundancy. Many essential results, obtained from digital aerial images, like DSM or DTM, classification maps, ortho-photos of any modality and 3D building models, are therefore derived cost-efficiently by fully automated 1 processing pipelines. 2.3 Digital Surface Model Estimating depth information and hence 3D structure of arbitrary scenes from overlapping views is essential and therefore a well-studied problem [Hartley and Zisserman, 2000, Kraus, 2004]. In this thesis we focus on purely image-based approaches, where we extensively make use of available multi-view matching results computed from the highly overlapping digital aerial images. 1 Note that the term fully automatic refers to an absence of manual interventions possibly required in case of missing data or erroneous intermediate results.
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
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: 2.2. Redundancy 13 Figure 2.1: Geom
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
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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
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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
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Page 65 and 66: 3.7. Refined Labeling 49 high compu
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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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