DEM Generation steps in photogrammetry

1. DEM from Digital
Photogrammetry(Image
matching)
Digital Photogrammetry and DEM Generation

2. Digital photogrammetry
• Measurements in photograph in a digital environment for
acquiring the geo information.
• Photographs may be taken with either analogue or digital
camera.
• The photographs taken with analogue camera are converted in
digital system by scanning.
• High quality professional scanners are used for scanning.

3. Digital photogrammetric workstations (DPW)
• Photographs are processed by DPW.
• DPW is a set of software/hardware used for processing the
photographs and extraction of different types of geodata.
• One example LPS.
• Different photogrammetric processes (orientation, AT and
bundle block adjustment) can be carried out and several
products (DTM, orthophoto) can be generated.

4. Orientation
• Interior orientation – Camera Calibration data as input.
• Exterior orientation
• Relative orientation
• Absolute orientation

8. Aerial triangulation
• Aerial triangulation is the process of measuring points in
images or stereo models such that the orientations of these
photographs can be reconstructed
– Measurement of corresponding points in overlapping images
– Extension (densification) of control points in images
– Measurement of ground control points in images
– Estimation of orientation parameters of all images (block adjustment)

9. Independent model and bundle block

11. Mathematical model: independent

12. Mathematical model: bundle block

13. DTM generation from Digital Photogrammetry
Left image Right image
Stereo model
Orientation (interior, Exterior, AT)
Image matching (matching technique, parameters)
DSM
Editing/Filtering
Final DEM

14. • Process of finding matching points in the corresponding stereo
image pairs.
• The conjugate point in the corresponding image is determined by
comparing the similarity measure, e.g. brightness
• Similarity is measured by correlation coefficient
• Correlation coefficient varies from 0 to 1
• 0 = no match at all
• 1 = 100 % matched
Image Matching

15. Image matching

16. Main steps in image matching
• Select a matching point in one image,
• Find its conjugate point in the other image
• Compute 3D position of the matched point in object space

17. Types of matching: area based matching

18. Area based matching

19. Difference
• Cross correlation
– Only considers the gray value intensity for comparing the pixels in template and search window
– Deals with translation only
– Do not deal with the rotational aspect of the pixel
– Do not regards the geometry
– Less accurate
• Least square matching
– considers the geometric transformation in the corresponding images
– The template looks for gray value change and translation as well as the rotational parameters and scale
change between the image patch
– based on the principle of least
– determines the accurate estimation of affine parameters and corrects the perspective geometry of the
images
– More accurate

20. Feature based matching
• Distinct features like points, edges and patches are
independently extracted in all images
• They are matched with the corresponding feature entities of
the other image
• Feature attributes and consistency in location of corresponding
feature is compared

21. • Least square feature based matching method identifies distinct
points as features to be matched in images
• these points are extracted in corresponding images
• initial correspondence is established by calculating correlation
coefficient
• affine transformation parameters are calculated for robust
least square adjustment
Feature based matching

22. Steps in DTM generation
• Mass point generation by image matching
• 3D Ground coordinates are determined
• Interpolation between the mass points for dense points
• DSM
• Filtering process are applied for reducing the height point to
the ground

23. Parameters influencing the matching results
• Matching technique/algorithm
• Search window
• Target window
• Correlation coefficient
• DTM Cell size
• Interpolation methods

24. Constraints
• To make the search process faster and efficient, different
constraints may be applied for image matching
– Image pyramid
– Epipolar geometry

25. Image pyramid
Reducing complexity/increasing reliability

26. Matching in image pyramid

27. Epipolar geometry

28. Epipolar geometry

29. Epipolar geometry

30. Problems in image matching