EuroSDR Projects - Host Ireland

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Bacher, U. and Mayer, H. (2005): Automatic Road Extraction from Multispectral High Resolution Satellite Images, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (36) 3/W24, 29-34. Baumgartner, A., Eckstein, W., Heipke, C., Hinz, S., Mayer, H., Radig, B., Steger, C. and Wiedemann, C. (1999a): T-REX: TUM Research on Road Extraction, Festschrift fur Prof. Dr.-Ing. Heinrich Ebner zum 60. Geburtstag, Lehrstuhl für Photogrammetrie und Fernerkundung der Technischen Universität München, 43-64. Baumgartner, A., Steger, C., Mayer, H., Eckstein, W., and Ebner, H. (1999b): Automatic Road Extraction Based on Multi-Scale, Grouping, and Context, Photogrammetric Engineering & Remote Sensing (65) 7: 777-785. Dick, A., Torr, P., and Cipolla, R. (2004): Modelling and Interpretation of Architecture from Several Images, International Journal of Computer Vision (60) 2: 111-134. Lacroix, V. and Acheroy, M. (1998): Feature-extraction Using the Constrained Gradient, ISPRS Journal of Photogrammetry and Remote Sensing (53), 85-94. Leibe, B. and Schiele, B. (2004): Combined Object Categorization and Segmentation with an Implicit Shape Model, ECCV’04 Workshop on Statistical Learning in Computer Vision, 1-15. Mayer, H., Hinz, S., Bacher, U., and Baltsavias, E. (2006): A Test of Automatic Road Extraction Approaches. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (34) B3. Mena, J.B and Malpica, J.A. (2003): Color Image Segmentation Using the Dempster-Shafer Theory of Evidence for the Fusion of Texture, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (34) 3/W8, 139-144. Mena, J.B and Malpica, J.A. (2005): An Automatic Method for Road Extraction in Rural and Semi- Urban Areas Starting from High Resolution Satellite Imagery, Pattern Recognition Letters (26), 1201- 1220. Scharstein, D. and Szeliski, R. (2002): A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms, International Journal of Computer Vision (47) 1: 7-42. Steger, C. (1998): An Unbiased Extractor of Curvilinear Structures, IEEE Transactions on Pattern Analysis and Machine Intelligence (20): 113-125. Stoica, R., Descombes, X., and Zerubia, J. (2004): A Gibbs Point Process for Road Extraction from Remotely Sensed Images, International Journal of Computer Vision (57) 2: 121-136. Wiedemann, C., Heipke, C., Mayer, H., and Jamet, O. (1998): Empirical Evaluation of Automatically Extracted Road Axes, CVPR Workshop on Empirical Evaluation Methods in Computer Vision, 172- 187. Wiedemann, C. and Hinz, S. (1999): Automatic Extraction and Evaluation of Road Networks from Satellite Imagery, International Archives of Photogrammetry and Remote Sensing (32) 3-2W5, 95- 100. 240
Zhang, C., 2004. Towards an Operational System for Automated Updating of Road Databases by Integration of Imagery and Geodata, ISPRS Journal of Photogrammetry and Remote Sensing (58): 166–186. Zhang, Q. and Couloigner, I. (2005a): Road Identification and Refinement on Multispectral Imagery Based on Angular Texture Signature, 25th EARSeL Symposium. Zhang, Q. and Couloigner, I. (2005b): Accurate Centerline Detection and Line Width Estimation of Thick Lines using the Radon Transform, Accepted for Publication in IEEE Transactions on Image Processing. Zhang, Q. and Couloigner, I. (2005c): Iterative and Localized Radon Transform for Accurate Road Centerline Detection from Classified Imagery, Submitted to Photogrammetric Engineering & Remote Sensing. Zhang, Q. and Couloigner, I. (2006a): Automated Road Network Extraction from High Resolution Multi-Spectral Imagery, ASPRS 2006 Annual Conference, Reno, Nevada, 10 pages. Zhang, Q. and Couloigner, I. (2006b): Benefit of the Angular Texture Signature for the Separation of Parking Lots and Roads on High Resolution Multi-spectral Imagery, Pattern Recognition Letters (27) 9: 937-946. 241
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European Spatial Data Research Nove
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PRESIDENT 2006 - 2008: Stig Jönsso
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H. Kaartinen and J. Hyyppä: EVALUA
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4.3.1 Data and study area..........
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2 QUESTIONNAIRES...................
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Abstract The objective of the EuroS
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The analysis of the structural qual
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Figure 2-3: Hermanni test site. Fig
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Espoonlahti Hermanni Senaatti Photo
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2.2 Reference Data 2.2.1 Field Meas
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Used data Time use Laser Aerial Gro
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Step 3: Import to CCModeler Figure
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Figure 3-4: Sequence of manual phot
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Figure 3-8: Workflow of laser scann
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Figure 3-11: Parametric building mo
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Each group of connected pixels clas
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Figure 3-18: Topological points and
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Detailed Description (Letters refer
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assumption is made: the two longest
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Figure 3-25: A 3D building model wi
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ICC used the same methods as Nebel
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where p75th is the value at the 75
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CyberCity Stuttgart Hamburg IGN ICC
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CyberCity Hamburg Stuttgart IGN ICC
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Height Cyber- City Hamburg Stuttgar
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Height Cyber- City Stuttgart IGN IC
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Height Cyber- City Stuttgart IGN IC
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Height Cyber- City HamburgStuttgart
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Height Cyber- City HamburgStuttgart
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All targets Eaves Ridges Height IGN
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CyberCity achieved a good quality i
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Point density, shadowing of trees a
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Height accuracy IQR [m] 1.6 1.4 1.2
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All Cyber- Ham- ICC laser+ Nebel+ I
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All Cyber- HamStutt- ICC laser+ Neb
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In building length determination (F
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degrees, std 6.3 degrees). In Senaa
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5 Discussion and Conclusions It can
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References Alharthy A. and Bethe, J
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Fraser, C.S., Baltsavias, E. and Gr
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Khoshelham K., 2004. Building Extra
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Sequeira, V., Ng, K., Wolfart, E.,
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Index of Figures Figure 2-1: Senaat
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Index of Tables Table 2-1: Aerial i
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90 Senaatti by Delft. Wireframe mod
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92 Espoonlahti by IGN, with and wit
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94 Senaatti by IGN, with and withou
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96 Hermanni by Aalborg.
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98 CyberCity ICC laser+aerial ICC l
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100 IGN FOI outlines Nebel+Partner
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Difference Images, Whole Test Site,
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Difference Images, Whole Test Site,
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Difference Images, Modelled Buildin
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Difference Images, Modelled Buildin
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EuroSDR-Project Commission 2 “Ima
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2 Project highlights The project Ch
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116 Feature Characteristics Object
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New are those combinations that ind
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120 Figure 4-1: Orthophotomosaic fr
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4.1.2 Segmentation The first step o
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124 Class Features Vegetation Ratio
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4.1.4 Change map As the results of
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Two types of changes were assigned
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130 Figure 4-11: Evaluation of chan
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132 Figure 4-13: Orthophotos of stu
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different villages were identified
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136 Figure 4-16: Change maps of Swi
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spectral reflectance. Shadows (blac
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140 Figure 4-19: Change map of Germ
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Figure 4-21: Diagnostics as given b
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144 Automatic Accepted Rejected Hum
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4.4.2 Segmentation, classification
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differentiated very well. The chang
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150 Figure 4-31: Ultracam real-colo
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152 Figure 4-35: Classification of
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154 Figure 4-38: Subset of change m
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5 Conclusions and Outlook In this p
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Annex 1 EuroSDR Change Detection Wo
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What is the impact of changing spat
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Annex 2 Paper presented at IGARSS05
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covering 2 x 2 km with reference da
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170 Red roof Bright object 0 20 40
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Annex 3 EuroSDR CD Workshop Nicosia
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176 method 1 indicates land cover c
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Annex 4 Comments on change map by B
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enzen mehr zu erwarten. Doch laut
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Abstract The EuroSDR “Sensor and
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2 Test Sites and Test Data The test
Page 191 and 192: Figure 3: Data set Fjärdhundra, ag
Page 193 and 194: The map of Sweden is only available
Page 195 and 196: 3.2 Accounting for Different Acquis
Page 197 and 198: 4 Analysis 4.1 Basic Information In
Page 199 and 200: 4.2.2 Linear Objects Since it is no
Page 201 and 202: 5 Results of Phase I 5.1 Test Site
Page 203 and 204: 5.1.2 Qualitative Comparison In add
Page 205 and 206: 140,0 120,0 100,0 80,0 60,0 40,0 20
Page 207 and 208: 5.3 Test Site Oberpfaffenhofen 5.3.
Page 209 and 210: 140,0 120,0 100,0 80,0 60,0 40,0 20
Page 211 and 212: cies in analysis. The following fig
Page 213 and 214: 5.4.2 Qualitative Comparison The ra
Page 215 and 216: Acknowledgments We thank all partic
Page 217: Index of Tables Table 1: Contest Ph
Page 221 and 222: Abstract Roads are important object
Page 223 and 224: overview over the whole area. We ra
Page 225 and 226: m and above no longer plays an impo
Page 227 and 228: 3 Test Data and Evaluation Criteria
Page 229 and 230: and straight enough. • Markus Ger
Page 231 and 232: Name (best) Test area Completeness
Page 233 and 234: • ADS40_1 and 2: Results for both
Page 235 and 236: • Ikonos1_Sub1: This image shows
Page 237 and 238: Figure 7: Ikonos3_Sub1 - Bacher; co
Page 239 and 240: Finally, we want to note some impor
Page 241: load nor response time for anybody
Page 246 and 247: objects, and their cooperation with
Page 248 and 249: Belgium, provided by their NMCAs. H
Page 250 and 251: Possibly only parts of images will
Page 252 and 253: How many people are employed in you
Page 254 and 255: 7. What is the average time differe
Page 256 and 257: - what is the degree of completenes
Page 259 and 260: Appendix 3: Questionnaire for Resea
Page 261 and 262: If others please specify: 2. What d
Page 263 and 264: Complex crossings (more than 4 road
Page 265 and 266: Combination of a) or b) with c) [0]
Page 267 and 268: If yes, using: DTM and / or [2] D
Page 269 and 270: Appendix 4: General Characteristics
Page 271: Most important features for practic
Page 274 and 275: IKONOS The images come from the SI
Page 277: Appendix 6: Documentation by Beumie
Page 281 and 282: Appendix 8: Documentation by Zhang
Page 283 and 284: LIST OF OEEPE/EuroSDR OFFICIAL PUBL
Page 285 and 286: 25 Ducher, G.: Test on Orthophoto a
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