Terrain Type Modeling for Automated Tie-point Detection in Aerial Photogrammetry

Photogrammetry consists in creating 3D models out of overlapping photos of an object (this object could be a crop seen by above, a building, a statue…). The 3D position of distinctive features on the object is determined via multiple observations of a set of Tie-points identified and matched in multiple images.

The texture of the object is fundamental for photogrammetry. For instance, it’s not possible to detect tie-points on an object without texture (water, sand), thus it’s not possible to create 3D models of these surfaces with photogrammetry. The goal of this semester-project is to quantify the quality of the texture of different materials (figure 1). Several quality estimators could be studied, for example, the number of tie-point and the precision of the determination of these tie-points in the image. The outcome of this project will permit to determine the precision of the final 3D model.

Figure 1: Order of magnitude of the number of tie-points detectable
between two consecutive photos for different type of terrains

The steps of this project may vary according to the student preferences:

  • Literature review about tie-points detection and matching (SIFT, SURF algorithm).
  • Experimentation with photogrammetric software (e.g., pix4D) or with computer vision toolbox (e.g., matlab)
  • Statistics on experimental data to draw out general principles (with matlab, python or R)

Recommended type of project:

Semester project (master students)

Work breakdown:

30% theory, 20% development, 50% experiments


Familiarity with computer-vision or photogrammetry, data-management with MATLAB, python or R


photogrammetry, computer-vision, image processing, statistics


Emmanuel Cledat