Volume IV-1/W1
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1/W1, 35-42, 2017
https://doi.org/10.5194/isprs-annals-IV-1-W1-35-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1/W1, 35-42, 2017
https://doi.org/10.5194/isprs-annals-IV-1-W1-35-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

  30 May 2017

30 May 2017

Monitoring of Building Construction by 4D Change Detection Using Multi-temporal SAR Images

C. H. Yang1, Y. Pang2, and U. Soergel1 C. H. Yang et al.
  • 1Institute for Photogrammetry, University of Stuttgart, Germany
  • 2Institute for Photogrammetry, University of Stuttgart, Germany

Keywords: Persistent Scatterer Interferometry (PSI), Synthetic Aperture Radar (SAR), Change Detection, Urban Monitoring, Time Series Analysis, Object-based Analysis

Abstract. Monitoring urban changes is important for city management, urban planning, updating of cadastral map, etc. In contrast to conventional field surveys, which are usually expensive and slow, remote sensing techniques are fast and cost-effective alternatives. Spaceborne synthetic aperture radar (SAR) sensors provide radar images captured rapidly over vast areas at fine spatiotemporal resolution. In addition, the active microwave sensors are capable of day-and-night vision and independent of weather conditions. These advantages make multi-temporal SAR images suitable for scene monitoring. Persistent scatterer interferometry (PSI) detects and analyses PS points, which are characterized by strong, stable, and coherent radar signals throughout a SAR image sequence and can be regarded as substructures of buildings in built-up cities. Attributes of PS points, for example, deformation velocities, are derived and used for further analysis. Based on PSI, a 4D change detection technique has been developed to detect disappearance and emergence of PS points (3D) at specific times (1D). In this paper, we apply this 4D technique to the centre of Berlin, Germany, to investigate its feasibility and application for construction monitoring. The aims of the three case studies are to monitor construction progress, business districts, and single buildings, respectively. The disappearing and emerging substructures of the buildings are successfully recognized along with their occurrence times. The changed substructures are then clustered into single construction segments based on DBSCAN clustering and α-shape outlining for object-based analysis. Compared with the ground truth, these spatiotemporal results have proven able to provide more detailed information for construction monitoring.