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DTM Generation From Analogue Maps

DTM Generation From Analogue Maps. By Varshosaz. Using cartographic data sources. Data digitised mainly from contour maps Digitising contours leads to oversampling over the contours and undersampling between the contours

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DTM Generation From Analogue Maps

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  1. DTM Generation From Analogue Maps By Varshosaz

  2. Using cartographic data sources • Data digitised mainly from contour maps • Digitising contours leads to oversampling over the contours and undersampling between the contours • Errors are inherent in paper maps due to drawing, generalisation, reproduction, etc. • May still be cost effective at medium or small scale with national coverage

  3. Using cartographic data sources (cont.) • Digitisation can be • Manual line following • Semi or fully automatic line following • Automatic raster scanning and vectorisation

  4. Manual digitising

  5. Converting a paper contour map into vector format • Scanning • Image type: TIF, GIF,BMP, etc. • Resolution • Removing noise • Median filter • Neighbourhood averaging • Contour detection • Edge detection • Binary extraction • Skeletonisation • Vectorisation (Contour Following)

  6. Scanning Maps • The scanning process converts the analogue (paper) maps into raster (digital) format. • Selection of an appropriate dpi for the scan is in essence the determining factor of how many dots per inch the scanner will record. • Limitations: • Scanning resolution of the scanner itself • Hardware issues and image file sizes.

  7. Scanners • Accuracy • Photogrammetric • Desktop Publishing • Mechanism • Flatbed • Drumbed

  8. Mechanism of Scanners

  9. Photogrammetric Scanners • Stable and Known Geometry • Accurate • Expensive • Limited Availability

  10. DeskTop Publishing Scanners • Everyday scanners • Cheap • High availability • Low accuracy and large distortions

  11. DTP: Distortions

  12. DTP: Distortion Removal

  13. Noise Removal • Noise is present in any scanned map due to: • Poor-sampling process • Poor original map. • Objective: remove unwanted noise before detecting, binarizingand vectorizing the contours. • Principle: Applying spatial domain smoothing techniques in local neighborhoods of the scanned map (image).

  14. Noise Removal: Median Filter • Sorting the intensity values in ascending or descending order. • Choose the median as new centre value. • Characteristics: • Removes pixels in the neighborhood that are dramatically different (noise) from the rest. • It does preserve sharpness of an image.

  15. Noise Removal: Median Filter

  16. Noise Removal: Neighborhood Averaging

  17. Contour Detection • Scanned contours are linear features, • They are bounded by “edges” (the transition or boundary between the contours and the background). • An edge is a discontinuity in the two dimensional grey scale function. • Abrupt change in the gray level intensity within an area of the image space constitutes an edge. • Contour detection (edge detection) refers to the process that examines the scanned map for discontinuities in the grey level function.

  18. Edge Detection • Edges are characterized by discontinuities in the gray values at their location. • A typical edge detection algorithm uses first derivative of an image eg Sobel

  19. Original Image

  20. Detected Edges

  21. Edge Detection: SOBEL Filter (1968)

  22. Binary Extraction • Objective: Reduce scanned map resolution from 256 intensities to two intensities. • Reduces the scanned map into two categories Contours and Background. • Applied to maps (images) that have been adequately enhanced, smoothed, and the contours have been detected as edges.

  23. Threshold Binary Extraction

  24. Edge Detection & Binarization

  25. Skeleton Processing (Thinning) • Gradient filtering and the binarizationproduce edges wider than one pixel. • The required final position of the edge lies roughly in the middle of this wider edge. • Extracting the center position of the edge is known as skeleton processing. • Based on a square array of image (3x3, 5x5, 7x7, etc). • Note that as the template size increases, the number of different combinations dramatically increases and so does the computation time.

  26. Skeleton Processing •Consider this 3x3 approach whichproduces a “skeleton line” which is close to a medial line.

  27. Skeleton Processing

  28. Skeleton Processing

  29. Contour Following • Output of Skeleton Processing: • Thin contour lines with one pixel width in the area of interest. • To extract the whole contour, we need to trace pixels and obtain their positions. • The vectorization processes usually done in semiautomatic mode, where the operator provides the initial points.

  30. Contour Following • The initial direction can either be given by the operator or be determined through the automatic search procedure. • In the latter case, the initial direction is actually approximated as 0 degrees (i.e., pointing upward in the scanned map). • User usually defines the number of search directions. • Example: define the initial direction as 0 degree and the number of directional matrices as 13.

  31. Contour Following

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