Volume XL-3/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W3, 153-160, 2015
https://doi.org/10.5194/isprsarchives-XL-3-W3-153-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W3, 153-160, 2015
https://doi.org/10.5194/isprsarchives-XL-3-W3-153-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  19 Aug 2015

19 Aug 2015

SEMANTIC INTERPRETATION OF INSAR ESTIMATES USING OPTICAL IMAGES WITH APPLICATION TO URBAN INFRASTRUCTURE MONITORING

Y. Wang1 and X. X. Zhu1,2 Y. Wang and X. X. Zhu
  • 1Helmholtz Young Investigators Group “SiPEO”, Technische Universität München, Arcisstraße 21, 80333 Munich, Germany
  • 2Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Oberpfaffenhofen, 82234 Weßling, Germany

Keywords: optical InSAR fusion, semantic classification, InSAR, SAR, railway monitoring, bridge monitoring

Abstract. Synthetic aperture radar interferometry (InSAR) has been an established method for long term large area monitoring. Since the launch of meter-resolution spaceborne SAR sensors, the InSAR community has shown that even individual buildings can be monitored in high level of detail. However, the current deformation analysis still remains at a primitive stage of pixel-wise motion parameter inversion and manual identification of the regions of interest. We are aiming at developing an automatic urban infrastructure monitoring approach by combining InSAR and the semantics derived from optical images, so that the deformation analysis can be done systematically in the semantic/object level. This paper explains how we transfer the semantic meaning derived from optical image to the InSAR point clouds, and hence different semantic classes in the InSAR point cloud can be automatically extracted and monitored. Examples on bridges and railway monitoring are demonstrated.