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Multiple image matching for Digital Elevation Model generation from air and spaceborne imagery.

Engineering & Physical Sciences
Office of Academic Affairs
Noh, Myoung-Jong
Licensing Manager
Zinn, Ryan

T2021-232 This invention creates a digital elevation model of the pictured ground from remote sensors by multiple stereo pair photogrammetric processing for increasing image usability, quality of the 3D surface model, and simplifying production process.
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The Need

A Digital Elevation Model (DEM) is a representation of the topographical surface of Earth. A DEM is often used to track climate change and create maps. DEMs are usually generated by active sensors such as LiDAR (Light Detection And Ranging) and passive sensors, such as imaging sensors. LiDAR DEMs must be acquired from airplanes, and thus have a high acquisition cost, limiting spatial and temporal coverage of the Earth surface. In contrast, satellite imagers provide data over the entire globe at a comparable spatial resolution to LiDAR (decimeters), with frequent repeats, allowing for surface change detection and elevation change measurement over large areas at low cost. The recent proliferation of satellite imagers, including constellations of hundreds of small, low-cost “small sats,” is dramatically increasing measurement volume, requiring highly efficient, automated systems for DEM processing. Further, due to their low weight and cost design, “small sats” have reduced data quality when compared to conventional, full-sized platforms. Further, these images typically have a small Field Of View and off-nadir angle, and thus do not have an appropriate geometry for producing DEMs from pairs of overlapping images, as conventionally performed. Thus, there is a need for a new processing approach that leverages the many repeat images “small sats” produce to improve DEM accuracy and quality.

The Technology

Dr. Myoung-Jong Noh of the Byrd Polar & Climate Research Center at Ohio State University has developed a technology for creating high-quality DEMs by multiple pairwise photogrammetric processing, effectively mitigating the effect of poor stereo imaging geometry, based on the Surface Extraction from Tin-based Search-space Minimization (SETSM) algorithm. The algorithm uses sets of overlapping images to create multiple pairs based on geometry and extracts the optimal height at each horizontal plane position through novel a weighted-pairwise and noise reduction processing approach. The system is fully automatic, requiring no user-defined parameters. Dr. Noh’s novel approach optimizes “small sats” repeat images to create a higher quality DEM.

Commercial Applications

  • Earth surface monitoring and planning
  • Civil engineering
  • 3D modeling industry
  • Resource extraction
  • Geotechnical mapping
  • Construction


  • Fully automated 3D surface reconstruction (Simplify the DEM generation process)
  • Increase usability of low-resolution and weak stereo-geometry images
  • Provide a high-quality DEM from low quality images

Research Interest

The Byrd Polar and Climate Research Center has expertise in developing remote sensing methods and datasets for the mapping and monitoring Earth surface change at a range of spatial scales. They utilize optical and multispectral imagery, laser and radar altimetry and Synthetic Aperture Radar data, as well as photogrammetric, geodetic, and interferometric techniques, to observe and understand Earth processes and humans impacts.

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