Semantic Interpretation of Digital Aerial Images Utilizing ...

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92 Chapter 4. From 3D to the Fusion of Redundant Pixel Observations chapter we have shown that a combination of appearance and 3D cues is essential for a reliable classification at the level of images. Since we are interested in a large-scale semantic interpretation of whole urban environments, we again fuse the intermediate results (now we have a probable class assignment) obtained for the original images within an orthographic view. Figure 4.3 depicts some redundant scene classifications projected to a common view point. Note that the dominant object class assignment is shown without a regularization procedure. It can be clearly seen that depending on the perspective camera view the mapped objects are classified with varying accuracy. Thus, we propose to fuse the raw confidences obtained from multiple view point in order to compute an improved interpretation result. In addition, a regularization may help to smooth the labeling with respect to the real object boundaries and to reduce the problem of wrongly assigned class labels. The used color coding for these object classes is given in Figure 3.13. 4.4.1 Accumulation of the Classifier Output In order to estimate a class-specific probability for each pixel in the orthographic view, computed class distributions (the multi-class RF, constructed as a supervised classfier, provides class distributions for every observed feature instance) from different view points are accumulated and then normalized for the number of multi-view interpretations. Note that compared to standard classifiers such, e.g., SVMs, where the outputs are distances to an estimated decision boundary, RFs inherently provide an efficient mapping of highdimensional data to a probabilistic output (forming a normalized class distribution). Therefore, an aggregating of multi-view results is straightforward. Figure 4.5 depicts the pixelwise accumulation result obtained by the proposed classification and fusion workflow. Due to classification and projection at the pixel level, the individual results shows a high granularity with respect to the dominant class confidences (first three rows), while the last row shows an improved semantic labeling. Nevertheless, the fusion step is performed at the level of pixels without considerations of important object boundaries and connected image regions. Thus, we introduce a regularization step to obtain a consistent final semantic labeling. 4.4.2 Refined Labeling Within the Orthographic View Although our proposed feature representation considers some local context information, collected within a small spatial neighborhood, each pixel in the original images and especially in the fused case are explained almost independently. The problem of obtaining a smooth and spatially consistent labeling of the whole image scene can be again seen as the task of multi-class image segmentation (see Chapter 3), where each pixel value is selected
4.4. Fusion of Redundant Classification Information 93 Figure 4.5: Semantic classification results projected to an orthographic view. The first three rows show the results obtained for redundant image tiles, while the last row depicts the aggregated confidences. The columns from left to right describe the confidences for building, water, grass, tree and street. The last column highlights a pixel-wise computation of the most dominant object class. Note that the pixel-wise fusion step considerably compensates for areas, where no interpretation is available (white pixels). In addition, the raw collection of multiple classification results significantly improves the final interpretation with respect to consistent assignment of object classes. from a predefined pool of class labels. In our case the labeling procedure is supported by fused class distributions. In general, a segmentation problem into multiple object classes can be defined as a minimization of the Potts model [Potts, 1952], which was originally introduced to model phenomena of solid state physics. Recall that Ω defines an arbitrary image domain in R W H , then the continuous formulation of the Potts model for a partition into N distinct object classes can be written as
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PhD Thesis Semantic Interpretation
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Statutory Declaration I declare tha
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This thesis was created at the Inst
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Kurzfassung Eines der grundlegenden
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Contents 1 Introduction 1 1.1 Aeria
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CONTENTS iii 5.4.3 Prototype Refine
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Chapter 1 Introduction Three-dimens
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1.2. Photo-realistic Modeling vs. V
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1.3. Semantic Interpretation of Aer
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1.4. Contributions 7 Figure 1.4: Ov
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1.5. Outline 9 3. From Interpreted
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Chapter 2 Digital Aerial Imagery Th
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2.2. Redundancy 13 Figure 2.1: Geom
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2.3. Digital Surface Model 15 Figur
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2.3. Digital Surface Model 17 Since
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2.3. Digital Surface Model 19 Figur
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2.5. Orthographic Image Representat
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2.7. Summary 23 Dataset Nb of Image
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Chapter 3 From Appearance and 3D to
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3.2. Overview 27 Figure 3.2: Corres
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3.3. Related Work 29 tation workflo
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3.4. Feature Representation 31 a co
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3.4. Feature Representation 33 cons
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3.4. Feature Representation 35 d LE
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3.4. Feature Representation 37 wher
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3.4. Feature Representation 39 Figu
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Page 59 and 60: 3.4. Feature Representation 43 for
Page 61 and 62: 3.5. Randomized Forest Classifier 4
Page 63 and 64: 3.6. Introducing Segmentation for O
Page 65 and 66: 3.7. Refined Labeling 49 high compu
Page 67 and 68: 3.8. Experiments on Benchmark Datas
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Page 89 and 90: 3.10. Discussion and Summary 73 Fig
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Page 97 and 98: 4.1. Introduction 81 Figure 4.1: Th
Page 99 and 100: 4.2. Introducing a Common View 83 i
Page 101 and 102: 4.3. Fusion of Redundant Intensity
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Page 119 and 120: 4.5. Experiments 103 Figure 4.12: I
Page 121 and 122: 4.5. Experiments 105 As shown in Fi
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Page 131 and 132: 4.5. Experiments 115 the CRFs with
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Page 135 and 136: 4.5. Experiments 119 Dallas buildin
Page 143 and 144: Chapter 5 From Interpreted Regions
Page 145 and 146: 5.1. Introduction 129 Figure 5.1: T
Page 147 and 148: 5.3. Overview 131 cation techniques
Page 149 and 150: 5.4. Building Modeling based on Sup
Page 157 and 158: 5.5. Experiments 141 Dataset Image
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5.5. Experiments 143 Figure 5.9: 3D
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5.6. Discussion and Summary 145 Fig
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5.6. Discussion and Summary 147 Fig
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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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