CMRT09

Stilla, Uwe

Bibliographic data

Monograph

Persistent identifier:: 856955019

Author:: Stilla, Uwe

Title:: CMRT09

Sub title:: object extraction for 3D city models, road databases, and traffic monitoring ; concepts, algorithms and evaluation ; Paris, France, September 3 - 4, 2009 ; [joint conference of ISPRS working groups III/4 and III/5]

Scope:: X, 234 Seiten

Year of publication:: 2009

Place of publication:: Lemmer

Publisher of the original:: GITC

Identifier (digital):: 856955019

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: FUSION OF OPTICAL AND INSAR FEATURES FOR BUILDING RECOGNITION IN URBAN AREAS J. D. Wegner, A. Thiele, U. Soergel

Document type:: Monograph

Structure type:: Chapter

CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation a b c Figure 5. Results of building detection based on optical data (a), detected corner lines in the InSAR data (b), and of building detection based on InSAR and optical data (c) buildings. Such comer lines also appear in single SAR images and hence this approach is not limited to InSAR data. Further developed, this approach may be the basis for a change detection method after natural hazards like flooding and hurricanes. An optical image acquired before the hazard and SAR data acquired afterwards can be analyzed using the presented approach. A human interpreter would only have to check those buildings for damages that were not detected from both data sources. Hence, all buildings recognized from the combination of optical and SAR features, shown in red in Fig. 5c, would be classified as undamaged. Only buildings in the optical image that where not detected would have to be checked speeding up the entire damage assessment step significantly. Although first results are encouraging, further improvements have to be made. One main disadvantage of the presented classification approach is that its quality measures are not interpretable as probabilities in a Bayesian sense. Although many parameters have been learned from training data, parts of the approach are still ad-hoc. A next step will thus be the integration of the presented approach into a Bayesian framework. Furthermore, the differences of the sensor geometries should be used for further building recognition enhancement. Since the roofs of high buildings are displaced away from the sensor and parts of the façade appear in the image, roof regions have to be shifted towards the sensor in order to delineate building footprints. Such displacement also bears height information which may be used as an additional feature for building recognition. More height information may also be derived directly from the InSAR data. Finally, three-dimensional modelling of the scene could be accomplished based on the building footprints, a height hypothesis and maybe even the estimation of the roof type. An iterative joint classification and three-dimensional modelling in a Bayesian framework, including context information, will be the final goal of this project. 7. REFERENCES Inglada, J., and Giros, A. 2004. On the possibility of Automatic Multisensor Image Registration. IEEE Transactions on Geoscience and Remote Sensing, Vol. 42, No. 10, pp. 2104- 2120. Klein, L. A. 2004. Sensor and Data Fusion-A Tool for Information Assessment and Decision Making, 3rd ed., Bellingham, WA: SPIE Press, pp. 127-181. Mueller, S., and Zaum, D. W. 2005. Robust Building Detection in Aerial Images. IntArchPhRS, Vol. XXXVI, Part B2AV24, pp. 143-148. Soergel, U., Thiele, A., Cadario, E., Thoennessen, U. 2007. Fusion of High-Resolution InSAR Data and optical Imagery in Scenes with Bridges over water for 3D Visualization and Interpretation. In: Proceedings of Urban Remote Sensing Joint Event 2007 (URBAN2007), 6 pages. Thiele, A., Schulz, K., Thoennessen, U., Cadario, E. 2006. Orthorectification as Preliminary Step for the Fusion of Data from Active and Passive Sensor Systems. In: Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems 2006 (MFI2006), 6 pages. Thiele, A., Cadario, E., Schulz, K., Thoennessen, U., Soergel, U. 2007. Building Recognition From Multi-Aspect High- resolution InSAR Data in Urban Areas. IEEE Transactions on Geoscience and Remote Sensing, Vol. 45, No. 11, pp. 3583- 3593. Thiele, A., Cadario, E., Schulz, K., Thoennessen, U., Soergel, U. 2008. Reconstruction of residential buildings from multi aspect InSAR data. In: Proceedings of ESA-EUSC Workshop, Frascati, Italy, available http://earth.esa.int/rtd/Events/ESA- EUSC_2008/, 6p. Tupin, F., Maitre, H., Mangin, J.-F., Nicolas, J-M., Pechersky, E. 1998. Detection of Linear Features in SAR Images: Application to Road Network Extraction. IEEE Transactions on Geoscience and Remote Sensing, Vol. 36, No. 2, pp. 434-453. Tupin, F., and Roux, M. 2003. Detection of building outlines based on the fusion of SAR and optical features. ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 58, pp. 71-82. Tupin, F., and Roux, M. 2005. Markov Random Field on Region Adjacency Graph for the Fusion of SAR and Opitcal Data in Radargrammetric Applications. IEEE Transactions on Geoscience and Remote Sensing, Vol. 42, No. 8, pp. 1920- 1928. Wegner J.D., and Soergel, U. 2008. Bridge height estimation from combined high-resolution optical and SAR imagery. IntArchPhRS, Vol. XXXVII, Part B7-3, pp. 1071-1076. Xu, F., Jin, Y.-Q. 2007. Automatic Reconstruction of Building Objects From Multiaspect Meter-Resolution SAR Images. IEEE Transactions on Geoscience and Remote Sensing, Vol. 45, No. 7, pp. 2336-2353. 174

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