EuroSDR Projects - Host Ireland

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6 Conclusion – Phase I The results of all test sites show a consistent status of visual interpretation. The goal of the first phase was to compare optical versus SAR image interpretation. The question to be answered was whether it would be possible to obtain the same accuracy and quality when interpreting SAR instead of optical image data. In general all of the linear and areal features could be interpreted quite well in SAR images. The results for the four different test sites look similar. It is obvious that main roads and highways, i.e. big linear objects, can be detected and well located in both sensor imageries. Although it is no problem to locate big linear objects, classification appears to be much more difficult in SAR images, as railways were sometimes classified as streets. Smaller linear objects, such as roads, alleys and little rivers, lead to more errors when locating and classifying them in SAR images. While comparing areal areas, large contiguous areas are usually well detected while small areas seem to be barely detectable in SAR imagery, whereas they can be found in optical images. Nevertheless, large buildings can also be detected and well located in SAR while, as before, the smaller ones are not always located correctly. The same results are obtained with sport fields, bridges and several trees, only partially detected in SAR but nearly completely detected in optical imagery. Small forest areas are barely detectable in SAR because they don’t look like forest areas in the images (which may also depend on the experience of the interpreters). Another problem seems to be the detection of water surfaces: large ones can be located and classified well in SAR images, but smaller ones can not be found, unlike in optical images. Concerning the optical data there is no difference in performance between panchromatic and color images. This can be inferred from the results of Fjärdhundra and Copenhagen test sites, where the data are of the same resolution, whereas Fjärdhundra is panchromatic and Copenhagen color image data. Regarding the results for test site Trudering, it becomes obvious that a higher resolution leads to better results for interpretations of SAR images. Consequently, discrepancies between SAR and optical sensing are shrinking with increasing resolution of images. To answer one of the main questions of this contest, it seems that SAR performs as well as optical sensing for large areal and linear objects but is not satisfactory with respect to smaller objects. Even though optical imagery was resampled to the pixel size of SAR sensing, it allows much more detailed analysis if the resolution is about three or four meters. This changes when using SAR data with higher resolutions, at least as far as the test site of Trudering is concerned. Results could be even better when using modern SAR data with resolution in the range of decimeters, as then speckle filtering could be done intensively without losing the interpretability of the data. Without training the interpreters in finding small or thin objects of a certain kind they are not familiar with in SAR imagery, it is nearly impossible for them to extract those objects correctly. Due to the similarities between the human visual system and optical imaging, this is much easier when using optical images. The analysis shows that under the conditions set by the test regarding resolution of the data and experience of the interpreters SAR sensing alone is not satisfactory for mapping small objects of the size of a few pixels in one or two dimensions. 212
Acknowledgments We thank all participants of the contest for contributing their work and analyses. Further thanks belong to Gert König, Hartmut Lehmann and Andreas Reigber who helped to initiate the contest, as well as Sübeyde Demirok and Andreas Krause who finished their seminar thesis analysing a part of the data. Support of several authorities in providing data for the fusion contest, especially Intermap Corp. and Bayrisches Landesvermessungsamt München, IHR/DLR (E-SAR data), Aerosensing GmbH (AeS-1 data) as well as the DDRE (EmiSAR data) and KAMPSAX is gratefully acknowledged. References Albertz, J., 1970: Sehen und Wahrnehmen bei der Luftbildinterpretation, Bildmessung und Luftbildwesen 38, (1970), Karlsruhe. Bellmann, A., Hellwich, O., 2005: Sensor and Data Fusion Contest: Comparison of Visual Analysis between SAR- and Optical Sensors, in Publikationen der DGPF, Band 14, 2005, pp. 217-223 Hellwich, O., Heipke, C., Wessel, B., 2001: Sensor and Data Fusion Contest: Information for Mapping from Airborne SAR and Optical Imagery, in International Geoscience and Remote Sensing Symposium 01, Sydney. IEEE, 2001, vol. VI, pp. 2793-2795. Hellwich, O., Reigber, A., Lehmann, H., 2002: OEEPE Sensor and Data Fusion Contest: Test Imagery to Compare and Combine Airborne SAR and Optical Sensors for Mapping, in Publikationen der DGPF, Band 11, 2002, pp. 279-283. Lohmann, P., Jacobsen, K., Pakzad, K., Koch, A., 2004: Comparative Information Extraction from SAR and optical Imagery: IntArchPhRS. Band XXXV, Part B3. Istanbul, 2004, pp. 535-540. 213
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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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Page 187 and 188: Abstract The EuroSDR “Sensor and
Page 189 and 190: 2 Test Sites and Test Data The test
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Page 201 and 202: 5 Results of Phase I 5.1 Test Site
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Page 243: Zhang, C., 2004. Towards an Operati
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Combination of a) or b) with c) [0]
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If yes, using: DTM and / or [2] D
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Appendix 4: General Characteristics
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Most important features for practic
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IKONOS The images come from the SI
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Appendix 6: Documentation by Beumie
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Appendix 8: Documentation by Zhang
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LIST OF OEEPE/EuroSDR OFFICIAL PUBL
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25 Ducher, G.: Test on Orthophoto a
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