Semantic Interpretation of Digital Aerial Images Utilizing ...

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98 Chapter 4. From 3D to the Fusion of Redundant Pixel Observations # Observations # Observations # Observations 1 5 10 Undefined Pixels 10 % 25 % 50% 10% 25% 50% 10 % 25 % 50 % Noisy image 14.1 10.7 8.0 14.1 10.7 8.0 14.1 10.7 8.0 Median 14.1 10.7 8.0 26.4 24.9 18.5 29.4 28.5 25.9 Mean 14.1 10.7 8.0 25.2 24.1 18.4 28.0 27.2 25.0 TV-L 1 29.0 27.1 21.2 36.8 36.0 34.1 39.3 38.6 37.2 Table 4.1: Quantitative evaluation of the height field fusion with multiple observations and a different amount of noise level. The evaluation is given in terms of the PSNR [dB]. The TV-L 1 model obtains the best noise suppression. (a) Ground truth (b) 14.08 dB (c) 29.44 dB (d) 39.39 dB (e) Ground truth (f) 14.08 dB (g) 29.44 dB (h) 39.39 dB Figure 4.8: Some visual results computed from 10 distorted synthetic height observations. The first row shows the height model in the image space, while the second row depict the corresponding rendering. From left to right: the original height model, a noisy input observations, a result obtained with a median estimation and the fusion result of the TV-L 1 model. One can see that both the median and TV-L 1 model successfully reduces the outliers. However, we can notice that a regularization is essential for a reliable fusion of multiple height maps. map fusion since the model better supports piecewise affine image structures.
4.5. Experiments 99 Experiments on Real Height Data Next, we apply our TV-L 1 model to real-world height maps extracted from the largescale aerial imagery. Since no ground truth data is available for the three datasets Dallas, Graz and San Francisco we mainly concentrate on a visual inspection of the computed fusion results. Note that an quantitative evaluation could be performed by, e.g., comparing the result to a point cloud taken with LiDAR. In Figure 4.9 computed image tiles for Dallas are depicted. In order to handle the large amount of available data we divide the fusion into 1600 × 1600 pixels image patches. The 2.5D fusion result is computed from an average of 10 input observations. One can see that although the proposed model works fine for the majority of the challenging building structures, specular facades and insufficient overlap between the aerial images, causes massive artifacts (bottom of the third image), which cannot be recovered completely. Although an increased strength of regularization would account for such massive outliers, the structures of the remaining objects then get over-smoothed. The real-world height models given in this thesis are computed for λ = 1.0. Figure 4.10 illustrates the fusion result obtained for a single scene of Graz. The images provide a pixel size of approximately 8 cm. From the rendering one can see that fine details, such as dormers, are nicely preserved, while the major rooftop structures are correctly delineated. Compared to the Dallas scene, the isolated high-rise building is captured entirely since there are no neighboring building structures, which annoy the correspondence computation in the dense matching process. A computed strip for San Francisco, consisting of 10×3 individually processed image patches, is shown in Figure 4.11. Although San Francisco is embedded in a hilly terrain the surface model can be reconstructed nicely. A tight fusion of a single image patch, representing color and height information, requires less than 2 minutes on a standard graphics card. Note that our large-scale workflow can be applied to process any quantity of input images due to the block-processing in smaller image tiles. 4.5.2 Experiments on Color Fusion The next step of our holistic interpretation workflow aims at fusing the available color information into an orthographic view. We therefore apply our convex fusion model, including the wavelet-based regularization, to generate high-quality real-world ortho-images. In this section we again apply our models (Equation (4.5) and (4.15)) to synthetic data, then we use it for integrating redundant color observations. To show the benefit of the waveletbased regularization, we compare the obtained results to those of the TV-L 1 model, the non-regularized mean and median fusion.
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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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3.4. Feature Representation 41 Figu
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3.4. Feature Representation 43 for
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3.5. Randomized Forest Classifier 4
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Page 65 and 66: 3.7. Refined Labeling 49 high compu
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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
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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
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Page 157 and 158: 5.5. Experiments 141 Dataset Image
Page 159 and 160: 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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