EXEMPLAR/PDE-BASED TECHNIQUE TO FILL NULL REGIONS AND CORRESPONDING ACCURACY ASSESSMENT

• US 20080319723A1
• Filed: 01/30/2008
• Published: 12/25/2008
• Est. Priority Date: 02/12/2007
• Status: Active Grant

• Current Assignee: Harris Corporation (L3Harris Technologies, Inc.)
• Original Assignee: Harris Corporation (L3Harris Technologies, Inc.)
• Inventors: Rahmes, Mark, Yates, Harlan, Allen, Josef, Smith, Anthony O'Neil, Kelley, Patrick, Berisford, Kathleen
• Granted Patent: US 7,881,913 B2
• Time in Patent Office: Days
• US Class Current: 703/2
• CPC Class Codes: G06T 17/05 Geographic models

Abstract

A geospatial modeling system may include a geospatial data storage device, and a processor cooperating with the geospatial data storage device for selectively inpainting data into at least one void in geospatial model data using a partial differential equation (PDE) algorithm and an exemplar algorithm.

Claims

1. A geospatial modeling system comprising:

a geospatial data storage device; and

a processor cooperating with said geospatial data storage device for selectively inpainting data into at least one void in geospatial model data using a partial differential equation (PDE) algorithm and an exemplar algorithm.

2. The geospatial modeling system of claim 1 wherein said processor further determines a void complexity predictor metric for the at least one void based upon a perimeter of the at least one void, and selects between the PDE algorithm and the exemplar algorithm based upon the void complexity predictor metric.

3. The geospatial modeling system of claim 2 wherein said processor selects between the PDE algorithm and the exemplar algorithm based upon whether the void complexity predictor metric is greater than a data frequency threshold.

4. The geospatial modeling system of claim 3 wherein said processor selects the exemplar algorithm if the void complexity predictor is greater than the data frequency threshold, and otherwise selects the PDE algorithm.

5. The geospatial modeling system of claim 3 wherein said processor initially selects and iteratively uses the exemplar algorithm until the void complexity predictor is less than the data frequency threshold, and then selects and iteratively uses the PDE algorithm.

6. The geospatial modeling system of claim 1 wherein said processor selects between the PDE algorithm and the exemplar algorithm based upon a size of the geospatial model data.

7. The geospatial modeling system of claim 1 wherein said processor selects between the PDE algorithm and the exemplar algorithm based upon an error level of a candidate exemplar source fill region.

8. The geospatial modeling system of claim 1 wherein the geospatial model data comprises elevation data.

9. The geospatial modeling system of claim 8 wherein said processor uses the PDE algorithm to inpaint by propagating elevation contour data from outside the at least one void along a direction of lines of constant elevation contour from outside the at least one void into the at least one void.

10. The geospatial modeling system of claim 8 wherein said processor iteratively propagates the elevation contour data from outside the at least one void into the at least one void.

11. The geospatial modeling system of claim 1 wherein selectively inpainting comprises iteratively inpainting data into the at least one void until a stopping value associated with the at least one void is below an error threshold.

12. The geospatial modeling system of claim 1 wherein said processor receives as an input geospatial model data containing both geospatial model terrain data and geospatial model cultural feature data and extracts and processes the geospatial model cultural feature data.

13. The geospatial modeling system of claim 1 wherein the PDE algorithm comprises at least one turbulent fluid flow modeling algorithm.

14. A geospatial modeling system comprising:

a geospatial data storage device for storing geospatial model data; and

a processor cooperating with said geospatial data storage device for

determining a void complexity predictor metric for least one void in the geospatial model data based upon a perimeter of the at least one void,

selecting between a partial differential equation (PDE) algorithm and an exemplar algorithm based upon the void complexity predictor metric,

inpainting data into the at least one void in the geospatial model data using the selected algorithms and

repeating determining, selecting, and inpainting until a stopping value associated with the at least one void is below an error threshold.

15. The geospatial modeling system of claim 14 wherein said processor selects between the PDE algorithm and the exemplar algorithm based upon whether the void complexity predictor metric is greater than a data frequency threshold.

16. The geospatial modeling system of claim 15 wherein said processor selects the exemplar algorithm if the void complexity predictor is greater than the data frequency threshold, and otherwise selects the PDE algorithm.

17. The geospatial modeling system of claim 15 wherein said processor initially selects and iteratively uses the exemplar algorithm until the void complexity predictor is less than the data frequency threshold, and then selects and iteratively uses the PDE algorithm.

18. The geospatial modeling system of claim 14 wherein said processor selects between the PDE algorithm and the exemplar algorithm based upon a size of the geospatial model data.

19. A geospatial modeling method comprising:

providing geospatial model data; and

selectively inpainting data into at least one void in the geospatial model data using a partial differential equation (PDE) algorithm and an exemplar algorithm.

20. The geospatial modeling method of claim 19 further comprising:

determining a void complexity predictor metric for the at least one void based upon a perimeter of the at least one void; and

selecting between the PDE algorithm and the exemplar algorithm based upon the void complexity predictor metric.

21. The geospatial modeling method of claim 20 wherein selecting comprises selecting between the PDE algorithm and the exemplar algorithm based upon whether the void complexity predictor metric is greater than a data frequency threshold.

22. The geospatial modeling method of claim 21 wherein selecting comprises selecting the exemplar algorithm if the void complexity predictor is greater than the data frequency threshold, and otherwise selecting the PDE algorithm.

23. The geospatial modeling method of claim 22 wherein selectively inpainting comprises iteratively inpainting the at least one void;

and wherein selecting comprises iteratively selecting the exemplar algorithm until the void complexity predictor is less than the data frequency threshold, and thereafter iteratively selecting the PDE algorithm.

24. The geospatial modeling method of claim 20 wherein selecting comprises selecting between the PDE algorithm and the exemplar algorithm based upon a size of the geospatial model data.

25. The geospatial modeling method of claim 19 wherein selectively inpainting comprises iteratively inpainting data into the at least one void until a stopping value associated with the at least one void is below an error threshold.

26. A computer-readable medium having computer-executable instructions for causing a computer to perform at least one step comprising:

selectively inpainting data into at least one void in geospatial model data using a partial differential equation (PDE) algorithm and an exemplar algorithm.

27. The computer-readable medium of claim 26 having further computer-executable instructions for causing the computer to perform steps comprising:

determining a void complexity predictor metric for the at least one void based upon a perimeter of the at least one void; and

selecting between the PDE algorithm and the exemplar algorithm based upon the void complexity predictor metric.

28. The computer-readable medium of claim 26 wherein selecting comprises selecting between the PDE algorithm and the exemplar algorithm based upon a size of the geospatial model data.

29. The computer-readable medium of claim 26 wherein selectively inpainting comprises iteratively inpainting data into the at least one void until a stopping value associated with the at least one void is below an error threshold.