1. Digital Terrain Modeling
Photogrammetric Data Acquisition By
M. Varshosaz

2. Photogrammetry: 3-D information from 2-D Imagery

3. DTM by Photogrammetry

4. DTM Generation

5. a) by (direct) “stereo geo-referencing”
Computing Elevation
a) by (direct) “stereo geo-referencing”
(x,y)l
(x,y)r
(X,Y,Z) 5

6. Photogrammetric Data Capture
Based on stereoscopic interpretation of aerial and/or satellite imagery.
Photogrammetric sampling techniques:
Regular sampling patterns,
Progressive sampling,
Selective sampling,
Composite sampling,
Measuring contour lines, and

7. Photogrammetric DTM Generation
Analytical
Using optical electro-mechanical systems
Operator sets up the model
Using Grid measurement or contour following techniques
Operator-Based; hence time consuming and error prone
Digital
Semi-automatic
Similar to analytical techniques
Still operator-based
Automatic 7 7

8. Photogrammetric techniques (cont.)
Automatic digital systems
Aim
To replace the operator by the Computer
To improve speed
Based on stereo-matching techniques 8 8

9. Digital Image Matching
Objective:
Automatic matching of conjugate points and/or entities in overlapping images.
Applications include:
Automatic relative orientation.
Automatic aerial triangulation.
Automatic DEM generation.
Automatic ortho-photo generation.

10. Image matching techniques
Area based
Tries to match areas in one image with their corresponding areas in the other (patch matching)
Feature based
Relations between objects are used to match features 10 10

11. Image Matching

12. Area Based Matching

13. Area Based Matching
Gray level distributions in small areas (image patches) in the two images of a stereo pair are matched.
Similarity measures between the image patches can be computed using:
Correlation coefficient.
Least squares matching.
Area based matching techniques are quite popular in photogrammetry.

14. Image Matching

15. Correlation Coefficient
Assuming that:
gl(x, y) is the gray value function within the template
In the left image.
gr(x, y) is the gray value function within matching
window inside the search window in the right image.
(nxm) is the size of the template and the matching windows.
Then, the cross correlation coefficient (similarity measure) can be computed as follows:

16. Correlation Coefficient

17. Cross Correlation Factor
The cross correlation factor might take values that range from -1 to +1.
ρ= 0 indicates no similarity at all.
ρ= -1 indicates an inverse similarity (e.g. similarity between the diapositive and the negative of the same image).
ρ= 1 indicates a perfect match (the highest similarity possible).

18. Correlation Coefficient
The cross correlation factor is computed for every possible position of the matching window within the search window.
The position of the conjugate point is determined by the location of the maximum correlation factor.
We will only accept correlation coefficients that are above a predetermined threshold (e.g. 0.5).

19. Correlation Matching Main disadvantage:
We do not compensate for any geometric or radiometric differences between the template and the matching windows.
Geometric differences will happen due to different scale and rotation parameters between the two images, foreshortening, etc.
Radiometric differences will happen due to different illumination conditions.
Need more sophisticated techniques

20. Problems Some problems that complicate the matching problem include:
Scale differences between the two images.
Different rotation angles between the two images.
Tilted surfaces (foreshortening problem).
Occlusions.
Relief displacement (different background).
Different illumination conditions between the two images (different gray values).

21. Scale Differences

22. Foreshortening Problem

23. Occlusions

24. Occlusions

25. Occlusions & Foreshortening

26. Relief Displacement (Different Background)

27. Relief Displacement (Different Background)