Sinkhole Detection, Landslide and Bridge Monitoring for Transportation Infrastructure by Automated Analysis of Interferometric Synthetic Aperture Radar Imagery
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

For very narrow results

When looking for a specific result

Best used for discovery & interchangable words

Recommended to be used in conjunction with other fields



Document Data
Clear All
Clear All

For additional assistance using the Custom Query please check out our Help Page


Sinkhole Detection, Landslide and Bridge Monitoring for Transportation Infrastructure by Automated Analysis of Interferometric Synthetic Aperture Radar Imagery

Filetype[PDF-5.98 MB]

Select the Download button to view the document
This document is over 5mb in size and cannot be previewed


  • Creators:
  • Corporate Creators:
  • Corporate Contributors:
  • Subject/TRT Terms:
  • Publication/ Report Number:
  • Resource Type:
  • Geographical Coverage:
  • Edition:
    Final Report
  • Contracting Officer:
  • Corporate Publisher:
  • Abstract:
    During the two year project, a series of radar satellite data were collected and processed by TRE Canada scenes using interferometric synthetic aperture radar (InSAR) technology into ground displacement measurements. These displacement data, with accuracy below 1 cm and approaching 1 mm, provided the initial data with which to study phenomena affecting transportation in Virginia. The phenomena analyzed included subsidence due to sinkhole formation, movements due to landslides and rockslides, and bridge settlement. The study area was comprised by a 40km by 40km region near Middlebrook, Virginia. Additional data outside of Virginia (from Vancouver, Canada and Wink, Texas) provided by TRE Canada proved useful in prototyping and validating the image analysis algorithms. A focus of the project was the development of image analysis algorithms that take the InSAR data as input and provide outputs of detections that can be used in a decision support system (DSS) to identify potential hazards to transportation. Two main theoretical approaches were explored: a graph-theoretic approach and parametric approach. In the graph theory approach, regions of subsidence were identified by an optimization process. This approach however was limited and did not allow for an easy integration of the main feature offered by the InSAR acquisition: the displacement time history for each scatterer. The second more generalized parametric approach exploited the temporal dimension as well as the spatial data. This approach is based on the availability of models describing both the spatial and temporal behavior of the geophysical features of interest. The model parameters are used to generate a multidimensional space that is then scanned with user-defined resolution. For each point in the parameter space, a spatiotemporal template is reconstructed from the original model. This template is then used to scan the point cloud data set for regions matching the spatiotemporal behavior. This new parametric approach provides the flexibility necessary to allow extensibility to other geophysical phenomena of interest. Photogrammetry, LiDAR, as well as traditional surveying methods were used as comparison to the InSAR-driven results and these ground studies confirmed and validated the results achieved from remote sensing. This final report details a number of case studies and inspections performed by the Virginia Department of Transportation (VDOT) including cases of sinkhole formation, bridge settlement and rockslides. In terms of automated geohazard detection (as provided by the newly developed algorithms operating on the InSAR data acquired over Virginia), the ground studies show that about 78% of the cases identified by our algorithm present strong field evidence of subsidence
  • Format:
  • Funding:
  • Collection(s):
  • Main Document Checksum:
  • Download URL:
  • File Type:

Supporting Files

  • No Additional Files
More +

You May Also Like

Checkout today's featured content at